{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective  : To evaluate different retrievers used in llm-graph-builder application using RAGAS library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Installation \n",
    "#Other packages can be installed from requirement.txt\n",
    "\n",
    "!pip install ragas==0.1.14 seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre requisites to run this notebook\n",
    "A pdf file to be kept in files folder and same has to be uploaded in llm-graph-builder application"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspaces/llm-graph-builder/backend/src/shared/common_fn.py:89: LangChainDeprecationWarning: The class `HuggingFaceEmbeddings` was deprecated in LangChain 0.2.2 and will be removed in 1.0. An updated version of the class exists in the langchain-huggingface package and should be used instead. To use it run `pip install -U langchain-huggingface` and import as `from langchain_huggingface import HuggingFaceEmbeddings`.\n",
      "  embeddings = SentenceTransformerEmbeddings(\n",
      "/opt/conda/envs/myenv310/lib/python3.10/site-packages/sentence_transformers/cross_encoder/CrossEncoder.py:11: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from tqdm.autonotebook import tqdm, trange\n"
     ]
    }
   ],
   "source": [
    "#importing all necessary packages\n",
    "#keeping this notebook in experiements folder \n",
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from dotenv import load_dotenv\n",
    "sys.path.append(os.path.abspath('../backend'))\n",
    "from src.llm import get_llm\n",
    "from src.QA_integration import QA_RAG, create_neo4j_chat_message_history,get_chat_mode_settings,get_neo4j_retriever,create_document_retriever_chain,retrieve_documents,format_documents,clear_chat_history\n",
    "from langchain_core.messages import HumanMessage\n",
    "from langchain_community.graphs import Neo4jGraph\n",
    "from langchain_community.document_loaders import DirectoryLoader\n",
    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
    "from datasets import Dataset, Features, Sequence, Value\n",
    "from ragas.testset.generator import TestsetGenerator\n",
    "from ragas.testset.evolutions import simple, reasoning, multi_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python version :  3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0]\n",
      "Ragas version :  0.1.14\n",
      "langchain version :  0.2.14\n"
     ]
    }
   ],
   "source": [
    "#Check version of libraries installed as it is crucial to have specific version to run the code in codespace\n",
    "### Python version :  3.10.14\n",
    "### Ragas version :  0.1.14\n",
    "### langchain version :  0.2.14\n",
    "import sys\n",
    "import ragas\n",
    "import langchain\n",
    "print(\"Python version : \",sys.version)\n",
    "print(\"Ragas version : \", ragas.__version__)\n",
    "print(\"langchain version : \", langchain.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Loading environmental variables\n",
    "load_dotenv()\n",
    "\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
    "uri = os.getenv(\"NEO4J_URI\")\n",
    "userName = os.getenv(\"NEO4J_USERNAME\")\n",
    "password = os.getenv(\"NEO4J_PASSWORD\")\n",
    "database = os.getenv(\"NEO4J_DATABASE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/myenv310/lib/python3.10/site-packages/pypdf/_crypt_providers/_cryptography.py:32: CryptographyDeprecationWarning: ARC4 has been moved to cryptography.hazmat.decrepit.ciphers.algorithms.ARC4 and will be removed from this module in 48.0.0.\n",
      "  from cryptography.hazmat.primitives.ciphers.algorithms import AES, ARC4\n"
     ]
    }
   ],
   "source": [
    "#Loading file kept in files folder through langchain loader\n",
    "# Here we are using \"About Amazon.pdf\"\n",
    "loader = DirectoryLoader(\"files\")\n",
    "documents = loader.load() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If above code gives error in codespace then run following commands in terminal \n",
    "\n",
    "sudo apt-get update\n",
    "\n",
    "sudo apt-get install -y libgl1-mesa-glx \n",
    "\n",
    "pip install opencv-python\n",
    "\n",
    "pip install typing \n",
    "\n",
    "then restart the kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:ragas.testset.docstore:Filename and doc_id are the same for all nodes.\n",
      "Generating: 100%|██████████| 15/15 [03:48<00:00, 15.21s/it]\n"
     ]
    }
   ],
   "source": [
    "#Generating synthetic test set through RAGAS library\n",
    "\n",
    "generator_llm = ChatOpenAI(model=\"gpt-4\")\n",
    "critic_llm = ChatOpenAI(model=\"gpt-4\")\n",
    "embeddings = OpenAIEmbeddings()\n",
    "\n",
    "generator = TestsetGenerator.from_langchain(\n",
    "    generator_llm,\n",
    "    critic_llm,\n",
    "    embeddings\n",
    ")\n",
    "\n",
    "try: \n",
    "    testset = generator.generate_with_langchain_docs(documents, test_size=15, distributions={simple:0.3, reasoning: 0.3, multi_context: 0.4})\n",
    "except Exception as e:\n",
    "    print(\"Error: \",e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>contexts</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>evolution_type</th>\n",
       "      <th>metadata</th>\n",
       "      <th>episode_done</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>[�s largest book retailer operating both onlin...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>[{'source': 'files/About Amazon.pdf'}]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>How does the integrated focused cost leadershi...</td>\n",
       "      <td>[�s largest book retailer operating both onlin...</td>\n",
       "      <td>The integrated focused cost leadership/differe...</td>\n",
       "      <td>simple</td>\n",
       "      <td>[{'source': 'files/About Amazon.pdf'}]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>What is the competitive strategy of Barnes and...</td>\n",
       "      <td>[ online advertising and the security of consu...</td>\n",
       "      <td>The answer to given question is not present in...</td>\n",
       "      <td>simple</td>\n",
       "      <td>[{'source': 'files/About Amazon.pdf'}]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Why should online retailers like Amazon be car...</td>\n",
       "      <td>[ online advertising and the security of consu...</td>\n",
       "      <td>Online retailers like Amazon should be careful...</td>\n",
       "      <td>simple</td>\n",
       "      <td>[{'source': 'files/About Amazon.pdf'}]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How threatening are substitute products to Ama...</td>\n",
       "      <td>[ online advertising and the security of consu...</td>\n",
       "      <td>The threat of substitutes for Amazon is high. ...</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>[{'source': 'files/About Amazon.pdf'}]</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question  \\\n",
       "0  What issues did Amazon's Elastic Block Storage...   \n",
       "1  How does the integrated focused cost leadershi...   \n",
       "2  What is the competitive strategy of Barnes and...   \n",
       "3  Why should online retailers like Amazon be car...   \n",
       "4  How threatening are substitute products to Ama...   \n",
       "\n",
       "                                            contexts  \\\n",
       "0  [�s largest book retailer operating both onlin...   \n",
       "1  [�s largest book retailer operating both onlin...   \n",
       "2  [ online advertising and the security of consu...   \n",
       "3  [ online advertising and the security of consu...   \n",
       "4  [ online advertising and the security of consu...   \n",
       "\n",
       "                                        ground_truth evolution_type  \\\n",
       "0  In 2012, Amazon's Elastic Block Storage (EBS) ...         simple   \n",
       "1  The integrated focused cost leadership/differe...         simple   \n",
       "2  The answer to given question is not present in...         simple   \n",
       "3  Online retailers like Amazon should be careful...         simple   \n",
       "4  The threat of substitutes for Amazon is high. ...      reasoning   \n",
       "\n",
       "                                 metadata  episode_done  \n",
       "0  [{'source': 'files/About Amazon.pdf'}]          True  \n",
       "1  [{'source': 'files/About Amazon.pdf'}]          True  \n",
       "2  [{'source': 'files/About Amazon.pdf'}]          True  \n",
       "3  [{'source': 'files/About Amazon.pdf'}]          True  \n",
       "4  [{'source': 'files/About Amazon.pdf'}]          True  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataframe from generated test set\n",
    "\n",
    "generated_testdf = testset.to_pandas()\n",
    "generated_testdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"What issues did Amazon's Elastic Block Storage (EBS) service face in 2012 and how did it affect the company?\",\n",
       " 'How does the integrated focused cost leadership/differentiation strategy help competitors compete with Amazon in the book industry?',\n",
       " 'What is the competitive strategy of Barnes and Noble in the e-commerce industry?',\n",
       " 'Why should online retailers like Amazon be careful about disclosing online sales taxes in their advertisements?',\n",
       " \"How threatening are substitute products to Amazon's patented tech and customer service?\",\n",
       " \"What was the 2012 issue with Amazon's EBS and its proposed fix?\",\n",
       " \"What strategy led to Amazon's growth from a small bookstore to a $48B retail giant?\",\n",
       " 'What strategic shift helped Amazon evolve from a small bookstore to a $48B retail giant?',\n",
       " \"How has Amazon's M&A strategy, transitioning from an online bookstore to a multi-category platform, been shaped by competition?\",\n",
       " \"What strategies are Amazon's book competitors using for market share and how does it compare to Amazon's?\",\n",
       " 'What methods does ICPEN use for cross-border online consumer issues and its effect on retailers like Amazon?',\n",
       " \"What strategies do Amazon's book competitors use to attract price-sensitive customers and grow market share, leveraging online shopping and unique, cheap books?\",\n",
       " \"What strategies are Amazon's book rivals using for price-sensitive customers and market growth, and its impact on Amazon's long-term investment focus?\",\n",
       " \"What were the 2012 issues with Amazon's EBS at the U.S. East center and their impact on customer service and future investments?\",\n",
       " 'How do rivals target cost-conscious readers and compete with Amazon?']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_testdf['question'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions are of following types\n",
    "\n",
    "Simple : Straight forward question from the context\n",
    "\n",
    "Reasoning: Rewrite the question in a way that enhances the need for reasoning to answer it effectively.\n",
    "\n",
    "Multi-Context: Rephrase the question in a manner that necessitates information from multiple related sections or chunks to formulate an answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "evolution_type\n",
       "multi_context    6\n",
       "reasoning        5\n",
       "simple           4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generated_testdf['evolution_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optional - Saving generated testset for further use\n",
    "\n",
    "generated_testdf.to_csv(\"RAGAS_testset.csv\",index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optional if you are re running code you can start from here\n",
    "#generated_testdf = pd.read_csv(\"RAGAS_testset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we will call our QnA bot to answer same questions that are generated through test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_graph_database_connection(uri, userName, password, database):\n",
    "  enable_user_agent = os.environ.get(\"ENABLE_USER_AGENT\", \"False\").lower() in (\"true\", \"1\", \"yes\")\n",
    "  if enable_user_agent:\n",
    "    graph = Neo4jGraph(url=uri, database=database, username=userName, password=password, refresh_schema=False, sanitize=True,driver_config={'user_agent':os.environ.get('NEO4J_USER_AGENT')})  \n",
    "  else:\n",
    "    graph = Neo4jGraph(url=uri, database=database, username=userName, password=password, refresh_schema=False, sanitize=True)    \n",
    "  return graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Graph connection\n",
    "graph = create_graph_database_connection(uri, userName, password, database)\n",
    "\n",
    "#Chat modes\n",
    "CHAT_VECTOR_MODE = \"vector\"\n",
    "CHAT_FULLTEXT_MODE = \"fulltext\"\n",
    "CHAT_ENTITY_VECTOR_MODE = \"entity search+vector\"\n",
    "CHAT_VECTOR_GRAPH_MODE = \"graph+vector\"\n",
    "CHAT_VECTOR_GRAPH_FULLTEXT_MODE = \"graph+vector+fulltext\"\n",
    "CHAT_GLOBAL_VECTOR_FULLTEXT_MODE = \"global search+vector+fulltext\"\n",
    "\n",
    "Modes = [CHAT_VECTOR_MODE,CHAT_FULLTEXT_MODE,CHAT_ENTITY_VECTOR_MODE,CHAT_VECTOR_GRAPH_MODE,CHAT_VECTOR_GRAPH_FULLTEXT_MODE,CHAT_GLOBAL_VECTOR_FULLTEXT_MODE]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"What issues did Amazon's Elastic Block Storage (EBS) service face in 2012 and how did it affect the company?\",\n",
       " 'How does the integrated focused cost leadership strategy help competitors compete with Amazon in the book industry?',\n",
       " 'What is the competitive strategy of Barnes and Noble in the e-commerce industry?',\n",
       " 'Why should online retailers like Amazon be careful about disclosing online sales taxes in their advertisements?',\n",
       " \"How threatening are substitute products to Amazon's patented tech and customer service?\",\n",
       " \"What was the 2012 issue with Amazon's EBS and its proposed fix?\",\n",
       " \"What strategy led to Amazon's growth from a small bookstore to a $48B retail giant?\",\n",
       " 'What strategic shift helped Amazon evolve from a small bookstore to a $48B retail giant?',\n",
       " \"How has Amazon's M and A strategy, transitioning from an online bookstore to a multi-category platform, been shaped by competition?\",\n",
       " \"What strategies are Amazon's book competitors using for market share and how does it compare to Amazon's?\",\n",
       " 'What methods does ICPEN use for cross-border online consumer issues and its effect on retailers like Amazon?',\n",
       " \"What strategies do Amazon's book competitors use to attract price-sensitive customers and grow market share, leveraging online shopping and unique, cheap books?\",\n",
       " \"What strategies are Amazon's book rivals using for price-sensitive customers and market growth, and its impact on Amazon's long-term investment focus?\",\n",
       " \"What were the 2012 issues with Amazon's EBS at the U.S. East center and their impact on customer service and future investments?\",\n",
       " 'How do rivals target cost-conscious readers and compete with Amazon?']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating list of questions from generated testset\n",
    "Questions = generated_testdf['question'].to_list()\n",
    "Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_context(mode,session_id,question):\n",
    "    history = create_neo4j_chat_message_history(graph, session_id)\n",
    "    messages = history.messages\n",
    "    user_question = HumanMessage(content=question)\n",
    "    messages.append(user_question)\n",
    "    model = 'openai-gpt-3.5'\n",
    "    document_names = '[]'\n",
    "    llm, model_name = get_llm(model=model)\n",
    "    chat_mode_settings = get_chat_mode_settings(mode=mode)\n",
    "    retriever = get_neo4j_retriever(graph=graph, chat_mode_settings=chat_mode_settings, document_names=document_names)\n",
    "    doc_retriever = create_document_retriever_chain(llm, retriever)\n",
    "    docs = retrieve_documents(doc_retriever, messages) \n",
    "    context = format_documents(docs, model)\n",
    "    return context[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_chatbot_qna(uri, userName, password,database, model_name, mode, question):\n",
    "   \"\"\"Test chatbot QnA functionality for different modes.\"\"\"\n",
    "   print(question,mode)\n",
    "   session_id = int(Modes.index(mode)+1)\n",
    "   graph = create_graph_database_connection(uri, userName, password, database) \n",
    "   clear_chat_history(graph, session_id)\n",
    "   QA_n_RAG = QA_RAG(graph, model_name, question, '[]', session_id, mode)\n",
    "   generated_context = fetch_context(mode,session_id,question)\n",
    "   try:\n",
    "       assert len(QA_n_RAG['message']) > 20\n",
    "       return QA_n_RAG['message'],generated_context\n",
    "   except AssertionError as e:\n",
    "       print(\"Failed for mode : \",mode,\" session id :\",session_id, \"Error : \", e)\n",
    "       return QA_n_RAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_qna_pair(question, mode):  \n",
    "   answer,generated_context = test_chatbot_qna(uri, userName, password,database,model_name='openai-gpt-3.5', mode=mode, question=question)\n",
    "   return question, mode, answer,generated_context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This cell may take few minutes based on no of question and modes\n",
    "\n",
    "results = []\n",
    "results = [process_qna_pair(question, mode) for question in Questions for mode in Modes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "      <th>application_context</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>graph+vector</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question                   mode  \\\n",
       "0  What issues did Amazon's Elastic Block Storage...                 vector   \n",
       "1  What issues did Amazon's Elastic Block Storage...               fulltext   \n",
       "2  What issues did Amazon's Elastic Block Storage...   entity search+vector   \n",
       "3  What issues did Amazon's Elastic Block Storage...           graph+vector   \n",
       "4  What issues did Amazon's Elastic Block Storage...  graph+vector+fulltext   \n",
       "\n",
       "                                              answer  \\\n",
       "0  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "1  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "2  In 2012, Amazon's Elastic Block Storage (EBS) ...   \n",
       "3  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "4  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "\n",
       "                                 application_context  \n",
       "0  Document start\\nThis Document belongs to the s...  \n",
       "1  Document start\\nThis Document belongs to the s...  \n",
       "2  Document start\\nThis Document belongs to the s...  \n",
       "3  Document start\\nThis Document belongs to the s...  \n",
       "4  Document start\\nThis Document belongs to the s...  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retrieved_df = pd.DataFrame(results, columns=['question', 'mode', 'answer','application_context'])\n",
    "retrieved_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the asnwers given by bot are not appropriate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>mode</th>\n",
       "      <th>answer</th>\n",
       "      <th>application_context</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>I don't have that specific information availab...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>What was the 2012 issue with Amazon's EBS and ...</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>I don't have that information right now. Is th...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>What were the 2012 issues with Amazon's EBS at...</td>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>I don't have specific information regarding th...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>What were the 2012 issues with Amazon's EBS at...</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>I don't have that information right now. Would...</td>\n",
       "      <td>Document start\\nThis Document belongs to the s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             question  \\\n",
       "5   What issues did Amazon's Elastic Block Storage...   \n",
       "35  What was the 2012 issue with Amazon's EBS and ...   \n",
       "80  What were the 2012 issues with Amazon's EBS at...   \n",
       "83  What were the 2012 issues with Amazon's EBS at...   \n",
       "\n",
       "                             mode  \\\n",
       "5   global search+vector+fulltext   \n",
       "35  global search+vector+fulltext   \n",
       "80           entity search+vector   \n",
       "83  global search+vector+fulltext   \n",
       "\n",
       "                                               answer  \\\n",
       "5   I don't have that specific information availab...   \n",
       "35  I don't have that information right now. Is th...   \n",
       "80  I don't have specific information regarding th...   \n",
       "83  I don't have that information right now. Would...   \n",
       "\n",
       "                                  application_context  \n",
       "5   Document start\\nThis Document belongs to the s...  \n",
       "35  Document start\\nThis Document belongs to the s...  \n",
       "80  Document start\\nThis Document belongs to the s...  \n",
       "83  Document start\\nThis Document belongs to the s...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retrieved_df[retrieved_df['answer'].str.lower().str.startswith(\"i don't have\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Important : Above result shows certain questions were not answered by QnA bot.\n",
    "\n",
    "#Further analysis needs to be done here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86, 4)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We will only consider proper answers for analysis\n",
    "\n",
    "retrieved_df = retrieved_df[~retrieved_df['answer'].str.lower().str.startswith(\"i don't have\")]\n",
    "retrieved_df.to_csv('chatbot_answers.csv', index=False)\n",
    "retrieved_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optional if you are running code from here you can uncomment\n",
    "\n",
    "# retrieved_df = pd.read_csv(\"chatbot_answers.csv\")\n",
    "# retrieved_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "generated_testdf = generated_testdf.rename(columns={'contexts':'RAGAS_context'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generated test set columns :  Index(['question', 'RAGAS_context', 'ground_truth', 'evolution_type',\n",
      "       'metadata', 'episode_done'],\n",
      "      dtype='object')\n",
      "QnA Bot Retrieved set columns:  Index(['question', 'mode', 'answer', 'application_context'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(\"Generated test set columns : \",generated_testdf.columns)\n",
    "print(\"QnA Bot Retrieved set columns: \",retrieved_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86, 9)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.merge(retrieved_df,generated_testdf,on='question',how='left')\n",
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#optional code if next cell is giving error as \"ValueError: Dataset feature \"contexts\" should be of type Sequence[string]\"ArithmeticError\n",
    "\n",
    "# final_df['contexts'] = final_df['contexts'].apply(lambda x:[x] if isinstance(x,str) else x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EVALUATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['question', 'mode', 'answer', 'contexts', 'ground_truth', 'question_complexity'],\n",
       "    num_rows: 86\n",
       "})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating dataset for evaluation\n",
    "\n",
    "data_samples = {\n",
    "     \n",
    "    'question': final_df['question'].tolist(),\n",
    "    'mode':final_df['mode'].tolist(),\n",
    "    'answer': final_df['answer'].tolist(),\n",
    "    'contexts': final_df['application_context'].tolist(),\n",
    "    'ground_truth': final_df['ground_truth'].tolist(),\n",
    "    'question_complexity' : final_df['evolution_type'].tolist()\n",
    "}\n",
    "features = Features({\n",
    "   'question': Value('string'),\n",
    "   'mode': Value('string'),\n",
    "   'answer': Value('string'),\n",
    "   'contexts': Value('string'), \n",
    "   'ground_truth': Value('string'),\n",
    "   'question_complexity': Value('string')\n",
    "})\n",
    "dataset = Dataset.from_dict(data_samples, features=features)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting context into list of strings as it is required in next step\n",
    "\n",
    "def preprocess_function(example):\n",
    "    example[\"contexts\"] = [example[\"contexts\"]]\n",
    "    return example\n",
    "\n",
    "dataset = dataset.map(preprocess_function)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating: 100%|██████████| 430/430 [03:23<00:00,  2.11it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>mode</th>\n",
       "      <th>answer</th>\n",
       "      <th>contexts</th>\n",
       "      <th>ground_truth</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>context_precision</th>\n",
       "      <th>context_recall</th>\n",
       "      <th>faithfulness</th>\n",
       "      <th>answer_relevancy</th>\n",
       "      <th>answer_correctness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>vector</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>[Document start\\nThis Document belongs to the ...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.924069</td>\n",
       "      <td>0.769896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>fulltext</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>[Document start\\nThis Document belongs to the ...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.924026</td>\n",
       "      <td>0.797546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>[Document start\\nThis Document belongs to the ...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.950704</td>\n",
       "      <td>0.733410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>graph+vector</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>[Document start\\nThis Document belongs to the ...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.924069</td>\n",
       "      <td>0.845223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What issues did Amazon's Elastic Block Storage...</td>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>Amazon's Elastic Block Storage (EBS) service f...</td>\n",
       "      <td>[Document start\\nThis Document belongs to the ...</td>\n",
       "      <td>In 2012, Amazon's Elastic Block Storage (EBS) ...</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.844968</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            question                   mode  \\\n",
       "0  What issues did Amazon's Elastic Block Storage...                 vector   \n",
       "1  What issues did Amazon's Elastic Block Storage...               fulltext   \n",
       "2  What issues did Amazon's Elastic Block Storage...   entity search+vector   \n",
       "3  What issues did Amazon's Elastic Block Storage...           graph+vector   \n",
       "4  What issues did Amazon's Elastic Block Storage...  graph+vector+fulltext   \n",
       "\n",
       "                                              answer  \\\n",
       "0  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "1  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "2  In 2012, Amazon's Elastic Block Storage (EBS) ...   \n",
       "3  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "4  Amazon's Elastic Block Storage (EBS) service f...   \n",
       "\n",
       "                                            contexts  \\\n",
       "0  [Document start\\nThis Document belongs to the ...   \n",
       "1  [Document start\\nThis Document belongs to the ...   \n",
       "2  [Document start\\nThis Document belongs to the ...   \n",
       "3  [Document start\\nThis Document belongs to the ...   \n",
       "4  [Document start\\nThis Document belongs to the ...   \n",
       "\n",
       "                                        ground_truth question_complexity  \\\n",
       "0  In 2012, Amazon's Elastic Block Storage (EBS) ...              simple   \n",
       "1  In 2012, Amazon's Elastic Block Storage (EBS) ...              simple   \n",
       "2  In 2012, Amazon's Elastic Block Storage (EBS) ...              simple   \n",
       "3  In 2012, Amazon's Elastic Block Storage (EBS) ...              simple   \n",
       "4  In 2012, Amazon's Elastic Block Storage (EBS) ...              simple   \n",
       "\n",
       "   context_precision  context_recall  faithfulness  answer_relevancy  \\\n",
       "0                1.0             1.0         0.750          0.924069   \n",
       "1                1.0             1.0         0.625          0.924026   \n",
       "2                0.0             0.0         0.000          0.950704   \n",
       "3                1.0             1.0         1.000          0.924069   \n",
       "4                1.0             1.0         1.000          0.926690   \n",
       "\n",
       "   answer_correctness  \n",
       "0            0.769896  \n",
       "1            0.797546  \n",
       "2            0.733410  \n",
       "3            0.845223  \n",
       "4            0.844968  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluating using RAGAS\n",
    "\n",
    "from ragas.metrics import (\n",
    "    answer_relevancy,\n",
    "    faithfulness,\n",
    "    context_recall,\n",
    "    context_precision,\n",
    "    answer_correctness\n",
    ")\n",
    "\n",
    "from ragas import evaluate\n",
    "\n",
    "result = evaluate(\n",
    "    dataset,\n",
    "    metrics=[\n",
    "        context_precision,\n",
    "        context_recall,\n",
    "        faithfulness,\n",
    "        answer_relevancy,\n",
    "        answer_correctness,\n",
    "    ]\n",
    ")\n",
    "\n",
    "eval_df = result.to_pandas()\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "eval_df.to_csv(\"Final_Score.csv\",index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['question', 'mode', 'answer', 'contexts', 'ground_truth',\n",
       "       'question_complexity', 'context_precision', 'context_recall',\n",
       "       'faithfulness', 'answer_relevancy', 'answer_correctness'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th colspan=\"5\" halign=\"left\">context_precision</th>\n",
       "      <th colspan=\"3\" halign=\"left\">context_recall</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">answer_relevancy</th>\n",
       "      <th colspan=\"5\" halign=\"left\">answer_correctness</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>...</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>5</td>\n",
       "      <td>0.561905</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.944288</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>0.970096</td>\n",
       "      <td>0.028123</td>\n",
       "      <td>5</td>\n",
       "      <td>0.742496</td>\n",
       "      <td>0.523471</td>\n",
       "      <td>0.992269</td>\n",
       "      <td>0.170004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>5</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.967252</td>\n",
       "      <td>0.953466</td>\n",
       "      <td>0.985486</td>\n",
       "      <td>0.011735</td>\n",
       "      <td>5</td>\n",
       "      <td>0.803536</td>\n",
       "      <td>0.565711</td>\n",
       "      <td>0.943629</td>\n",
       "      <td>0.147004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.976775</td>\n",
       "      <td>0.950704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020596</td>\n",
       "      <td>4</td>\n",
       "      <td>0.537766</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.733410</td>\n",
       "      <td>0.251963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>6</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.939813</td>\n",
       "      <td>0.930814</td>\n",
       "      <td>0.955302</td>\n",
       "      <td>0.009615</td>\n",
       "      <td>6</td>\n",
       "      <td>0.803399</td>\n",
       "      <td>0.437395</td>\n",
       "      <td>0.995587</td>\n",
       "      <td>0.208676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.954965</td>\n",
       "      <td>0.934895</td>\n",
       "      <td>0.974246</td>\n",
       "      <td>0.014387</td>\n",
       "      <td>5</td>\n",
       "      <td>0.853349</td>\n",
       "      <td>0.619032</td>\n",
       "      <td>0.996704</td>\n",
       "      <td>0.187401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.969242</td>\n",
       "      <td>0.924026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032231</td>\n",
       "      <td>4</td>\n",
       "      <td>0.672318</td>\n",
       "      <td>0.175373</td>\n",
       "      <td>0.940164</td>\n",
       "      <td>0.339199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>5</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "      <td>5</td>\n",
       "      <td>0.511905</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.934088</td>\n",
       "      <td>0.900393</td>\n",
       "      <td>0.967728</td>\n",
       "      <td>0.029599</td>\n",
       "      <td>5</td>\n",
       "      <td>0.735539</td>\n",
       "      <td>0.609525</td>\n",
       "      <td>0.909184</td>\n",
       "      <td>0.123181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.953706</td>\n",
       "      <td>0.933291</td>\n",
       "      <td>0.966764</td>\n",
       "      <td>0.014332</td>\n",
       "      <td>4</td>\n",
       "      <td>0.727843</td>\n",
       "      <td>0.488364</td>\n",
       "      <td>0.846143</td>\n",
       "      <td>0.162029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.975329</td>\n",
       "      <td>0.973230</td>\n",
       "      <td>0.978811</td>\n",
       "      <td>0.003037</td>\n",
       "      <td>3</td>\n",
       "      <td>0.394594</td>\n",
       "      <td>0.172125</td>\n",
       "      <td>0.665870</td>\n",
       "      <td>0.250464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>6</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.930291</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>0.957943</td>\n",
       "      <td>0.026410</td>\n",
       "      <td>6</td>\n",
       "      <td>0.742180</td>\n",
       "      <td>0.491398</td>\n",
       "      <td>0.918170</td>\n",
       "      <td>0.188076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.764748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.974305</td>\n",
       "      <td>0.427745</td>\n",
       "      <td>5</td>\n",
       "      <td>0.858412</td>\n",
       "      <td>0.670062</td>\n",
       "      <td>0.996556</td>\n",
       "      <td>0.137474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.944675</td>\n",
       "      <td>0.914451</td>\n",
       "      <td>0.989475</td>\n",
       "      <td>0.033573</td>\n",
       "      <td>4</td>\n",
       "      <td>0.559433</td>\n",
       "      <td>0.174966</td>\n",
       "      <td>0.845223</td>\n",
       "      <td>0.300916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>6</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.942806</td>\n",
       "      <td>0.922784</td>\n",
       "      <td>0.955302</td>\n",
       "      <td>0.013306</td>\n",
       "      <td>6</td>\n",
       "      <td>0.736784</td>\n",
       "      <td>0.437216</td>\n",
       "      <td>0.990246</td>\n",
       "      <td>0.237939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.935275</td>\n",
       "      <td>0.892618</td>\n",
       "      <td>0.962012</td>\n",
       "      <td>0.026796</td>\n",
       "      <td>5</td>\n",
       "      <td>0.786999</td>\n",
       "      <td>0.455712</td>\n",
       "      <td>0.946683</td>\n",
       "      <td>0.190876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.972342</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.032359</td>\n",
       "      <td>4</td>\n",
       "      <td>0.643641</td>\n",
       "      <td>0.175082</td>\n",
       "      <td>0.934310</td>\n",
       "      <td>0.339174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>6</td>\n",
       "      <td>0.910714</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>0.935297</td>\n",
       "      <td>0.914959</td>\n",
       "      <td>0.955338</td>\n",
       "      <td>0.013688</td>\n",
       "      <td>6</td>\n",
       "      <td>0.813315</td>\n",
       "      <td>0.544215</td>\n",
       "      <td>0.991202</td>\n",
       "      <td>0.156961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0.956708</td>\n",
       "      <td>0.934895</td>\n",
       "      <td>0.985504</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>5</td>\n",
       "      <td>0.677124</td>\n",
       "      <td>0.475465</td>\n",
       "      <td>0.823299</td>\n",
       "      <td>0.126854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>0.924069</td>\n",
       "      <td>0.997744</td>\n",
       "      <td>0.031505</td>\n",
       "      <td>4</td>\n",
       "      <td>0.584535</td>\n",
       "      <td>0.175324</td>\n",
       "      <td>0.817812</td>\n",
       "      <td>0.292301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity context_precision  \\\n",
       "                                                                  count   \n",
       "0            entity search+vector       multi_context                 5   \n",
       "1            entity search+vector           reasoning                 5   \n",
       "2            entity search+vector              simple                 4   \n",
       "3                        fulltext       multi_context                 6   \n",
       "4                        fulltext           reasoning                 5   \n",
       "5                        fulltext              simple                 4   \n",
       "6   global search+vector+fulltext       multi_context                 5   \n",
       "7   global search+vector+fulltext           reasoning                 4   \n",
       "8   global search+vector+fulltext              simple                 3   \n",
       "9                    graph+vector       multi_context                 6   \n",
       "10                   graph+vector           reasoning                 5   \n",
       "11                   graph+vector              simple                 4   \n",
       "12          graph+vector+fulltext       multi_context                 6   \n",
       "13          graph+vector+fulltext           reasoning                 5   \n",
       "14          graph+vector+fulltext              simple                 4   \n",
       "15                         vector       multi_context                 6   \n",
       "16                         vector           reasoning                 5   \n",
       "17                         vector              simple                 4   \n",
       "\n",
       "                                 context_recall                      ...  \\\n",
       "        mean  min  max       std          count      mean       min  ...   \n",
       "0   0.800000  0.0  1.0  0.447214              5  0.561905  0.142857  ...   \n",
       "1   0.800000  0.0  1.0  0.447214              5  0.250000  0.000000  ...   \n",
       "2   0.500000  0.0  1.0  0.577350              4  0.500000  0.000000  ...   \n",
       "3   0.833333  0.0  1.0  0.408248              6  0.952381  0.714286  ...   \n",
       "4   1.000000  1.0  1.0  0.000000              5  1.000000  1.000000  ...   \n",
       "5   1.000000  1.0  1.0  0.000000              4  1.000000  1.000000  ...   \n",
       "6   0.800000  0.0  1.0  0.447214              5  0.511905  0.142857  ...   \n",
       "7   0.500000  0.0  1.0  0.577350              4  0.125000  0.000000  ...   \n",
       "8   0.666667  0.0  1.0  0.577350              3  0.666667  0.000000  ...   \n",
       "9   0.833333  0.0  1.0  0.408248              6  0.952381  0.714286  ...   \n",
       "10  1.000000  1.0  1.0  0.000000              5  1.000000  1.000000  ...   \n",
       "11  1.000000  1.0  1.0  0.000000              4  1.000000  1.000000  ...   \n",
       "12  0.833333  0.0  1.0  0.408248              6  0.952381  0.714286  ...   \n",
       "13  1.000000  1.0  1.0  0.000000              5  1.000000  1.000000  ...   \n",
       "14  1.000000  1.0  1.0  0.000000              4  1.000000  1.000000  ...   \n",
       "15  0.833333  0.0  1.0  0.408248              6  0.910714  0.714286  ...   \n",
       "16  1.000000  1.0  1.0  0.000000              5  1.000000  1.000000  ...   \n",
       "17  0.750000  0.0  1.0  0.500000              4  1.000000  1.000000  ...   \n",
       "\n",
       "   answer_relevancy                                          \\\n",
       "              count      mean       min       max       std   \n",
       "0                 5  0.944288  0.897861  0.970096  0.028123   \n",
       "1                 5  0.967252  0.953466  0.985486  0.011735   \n",
       "2                 4  0.976775  0.950704  1.000000  0.020596   \n",
       "3                 6  0.939813  0.930814  0.955302  0.009615   \n",
       "4                 5  0.954965  0.934895  0.974246  0.014387   \n",
       "5                 4  0.969242  0.924026  1.000000  0.032231   \n",
       "6                 5  0.934088  0.900393  0.967728  0.029599   \n",
       "7                 4  0.953706  0.933291  0.966764  0.014332   \n",
       "8                 3  0.975329  0.973230  0.978811  0.003037   \n",
       "9                 6  0.930291  0.897861  0.957943  0.026410   \n",
       "10                5  0.764748  0.000000  0.974305  0.427745   \n",
       "11                4  0.944675  0.914451  0.989475  0.033573   \n",
       "12                6  0.942806  0.922784  0.955302  0.013306   \n",
       "13                5  0.935275  0.892618  0.962012  0.026796   \n",
       "14                4  0.972342  0.926690  0.999998  0.032359   \n",
       "15                6  0.935297  0.914959  0.955338  0.013688   \n",
       "16                5  0.956708  0.934895  0.985504  0.018364   \n",
       "17                4  0.968694  0.924069  0.997744  0.031505   \n",
       "\n",
       "   answer_correctness                                          \n",
       "                count      mean       min       max       std  \n",
       "0                   5  0.742496  0.523471  0.992269  0.170004  \n",
       "1                   5  0.803536  0.565711  0.943629  0.147004  \n",
       "2                   4  0.537766  0.172675  0.733410  0.251963  \n",
       "3                   6  0.803399  0.437395  0.995587  0.208676  \n",
       "4                   5  0.853349  0.619032  0.996704  0.187401  \n",
       "5                   4  0.672318  0.175373  0.940164  0.339199  \n",
       "6                   5  0.735539  0.609525  0.909184  0.123181  \n",
       "7                   4  0.727843  0.488364  0.846143  0.162029  \n",
       "8                   3  0.394594  0.172125  0.665870  0.250464  \n",
       "9                   6  0.742180  0.491398  0.918170  0.188076  \n",
       "10                  5  0.858412  0.670062  0.996556  0.137474  \n",
       "11                  4  0.559433  0.174966  0.845223  0.300916  \n",
       "12                  6  0.736784  0.437216  0.990246  0.237939  \n",
       "13                  5  0.786999  0.455712  0.946683  0.190876  \n",
       "14                  4  0.643641  0.175082  0.934310  0.339174  \n",
       "15                  6  0.813315  0.544215  0.991202  0.156961  \n",
       "16                  5  0.677124  0.475465  0.823299  0.126854  \n",
       "17                  4  0.584535  0.175324  0.817812  0.292301  \n",
       "\n",
       "[18 rows x 27 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visual_df = eval_df.groupby(['mode','question_complexity'])[['context_precision','context_recall','faithfulness', 'answer_relevancy','answer_correctness']].agg(['count','mean','min','max','std']).reset_index()\n",
    "visual_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysing each metric "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Faithfulness\n",
    "\n",
    "This measures the factual consistency of the generated answer against the given context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.347500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.296911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.530327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.330579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.383333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.738264</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.171521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.906494</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.751488</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.110986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.493839</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.235594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.537085</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.168360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.194444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.173472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.810311</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.208700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.746032</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.297804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.844024</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.892857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.726115</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.906667</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.136219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity      mean       min  \\\n",
       "0            entity search+vector       multi_context  0.347500  0.000000   \n",
       "1            entity search+vector           reasoning  0.530327  0.000000   \n",
       "2            entity search+vector              simple  0.383333  0.000000   \n",
       "3                        fulltext       multi_context  0.738264  0.545455   \n",
       "4                        fulltext           reasoning  0.906494  0.714286   \n",
       "5                        fulltext              simple  0.751488  0.625000   \n",
       "6   global search+vector+fulltext       multi_context  0.493839  0.142857   \n",
       "7   global search+vector+fulltext           reasoning  0.537085  0.300000   \n",
       "8   global search+vector+fulltext              simple  0.194444  0.000000   \n",
       "9                    graph+vector       multi_context  0.810311  0.375000   \n",
       "10                   graph+vector           reasoning  0.906667  0.533333   \n",
       "11                   graph+vector              simple  0.746032  0.428571   \n",
       "12          graph+vector+fulltext       multi_context  0.844024  0.636364   \n",
       "13          graph+vector+fulltext           reasoning  0.920000  0.600000   \n",
       "14          graph+vector+fulltext              simple  0.892857  0.571429   \n",
       "15                         vector       multi_context  0.726115  0.500000   \n",
       "16                         vector           reasoning  0.906667  0.700000   \n",
       "17                         vector              simple  0.708333  0.333333   \n",
       "\n",
       "         max       std  \n",
       "0   0.800000  0.296911  \n",
       "1   0.900000  0.330579  \n",
       "2   1.000000  0.433333  \n",
       "3   0.928571  0.171521  \n",
       "4   1.000000  0.133204  \n",
       "5   0.888889  0.110986  \n",
       "6   0.800000  0.235594  \n",
       "7   0.687500  0.168360  \n",
       "8   0.333333  0.173472  \n",
       "9   1.000000  0.233402  \n",
       "10  1.000000  0.208700  \n",
       "11  1.000000  0.297804  \n",
       "12  1.000000  0.147066  \n",
       "13  1.000000  0.178885  \n",
       "14  1.000000  0.214286  \n",
       "15  1.000000  0.203947  \n",
       "16  1.000000  0.136219  \n",
       "17  1.000000  0.276385  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faithfulness_df = eval_df.groupby(['mode', 'question_complexity']).agg({\n",
    "   'faithfulness': ['mean', 'min', 'max', 'std']\n",
    "}).reset_index()\n",
    "faithfulness_df.columns = ['mode', 'question_complexity', 'mean', 'min', 'max', 'std']\n",
    "faithfulness_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lsGLFCnTr1k3jNStWrIC5ublGq1Ii+glCRESJ8v79ewEgfn5+Ws+9fftWXr58qTzCwsKU51q0aCEApG/fvhqv2bRpkwCQESNGaCyvV6+eqFQquX37toiI3Lt3TwDI4sWLtT4XgAwePFj5e/DgwQJAatasqVHujz/+EABy8eJFERG5f/++GBoaysiRIzXK/fvvv5IuXTpleXh4uGTNmlXc3d3ly5cvSrl58+YJAPH09Izn2/ofR0dHAaD1KFOmjLx69UqjbMzvTW3VqlUCQA4fPqxVz8aNG3/380VEMmXKJIULF05Q2Zgxx/zM4OBgMTY2lh49eijLPn/+LFFRURqvvXfvnhgbG8uwYcOUZQcOHBAA4uzsrFXHtWvXCgA5cOBAgmJzd3eXrFmzyuvXr5VlFy9eFAMDA/H399f6zLVr1373PdVl43rcu3cvwfWMa1vt2LGjxHXaoS6bJUsWefPmjbJ88+bNAkC2bt2qLFu8eLEAkNOnT2u8h6Ojo7Ro0ULrvT09PTW2zbjiUu+XMeMXESlSpIh4eHgofx85ckQAyIoVKzTK7dq1S2P5xo0b44wxpi5duoiFhYVERkbGWyY+AKRjx47y5s0bMTIykmXLlomIyPbt20WlUsn9+/eV/eLly5ciIhIdHS158uQRHx8fiY6OVt4rLCxMcubMKZUqVVKWjR8/XlnfcX22gYGBXLlyJc7nYh6D/P39xcDAIM7vQR1D4cKFpVq1aon+DtTbgYeHh4SHhyvLx40bJwBk8+bNIiISFRUlOXLkkIYNG2q8ftKkSaJSqeTu3bvf/JwWLVpIxowZReTr8bhixYrK+9ra2srQoUOVbWr8+PHK67p27SoA5MiRI8qyDx8+SM6cOcXJyUnZh6ZMmSIAZM2aNUq50NBQyZ07t8axIDHrLy6RkZEax22Rr79V2bJlk1atWinLErMvuru7i52dnbx7905ZtmfPHgEgjo6O34xHXe+NGzfGW+bNmzcCQOrUqaMsi72NqcXe/4cPHy4ZM2aUmzdvapTr27evGBoaysOHD0UkYfvht47LsY8v6notX75cWRYeHi6lSpUSMzMzCQkJEZHEfc9xSczvWHBwsBgZGUnlypU1jt0zZswQALJo0SKN+gCQlStXKsuuX7+u7PcnT55Ulu/evVvrWJrQ8w6RH19nIiJz585VvueTJ0+KoaGhdO3aVeN16mNEzONYgQIF4jxXUb/ftWvXlGXh4eFibW0d5+8KEf0YdrUjIkqkkJAQAICZmZnWc15eXrCxsVEe6u5FMcVsQQF8HXTZ0NAQnTt31ljeo0cPiAh27tz5w7F27NhR4+9OnTopnwkAGzZsQHR0NBo0aIBXr14pD1tbW+TJk0fpinHmzBkEBwfj999/1xjXomXLlrC0tExwPCVKlMDevXuxd+9ebNu2DSNHjsSVK1dQs2ZNjVmjMmTIoPz/8+fPePXqFUqWLAkAOHfunNb7/v777wn6/JCQEK27s9/j6uqKcuXKKX/b2NjAxcUFd+/eVZYZGxsr43lFRUXh9evXMDMzg4uLS5zxtmjRQqOOifXs2TNcuHABLVu2RObMmZXlhQoVQqVKlZT1+6MGDRqkrCf1w9bWNtH1TIyGDRsiU6ZMyt/q7zzm96xLsbehcuXKaXz22rVrYWlpiUqVKmnsKx4eHjAzM1P2FXULwW3btsXZxUtdJjQ0NMFdyuKSKVMm+Pr6Kt1AVq5cidKlS8c5mcGFCxeUbmGvX79WYg8NDUXFihVx+PBhrS5I8fH09ISrq+s3y0RHR2PTpk2oUaMGihUrpvW8usWilZUVrly5glu3biXos2Nr166dRmuNDh06IF26dMr2b2BggKZNm2LLli348OGDUm7FihUoXbq0RivL72nSpAkOHjyotJZ6/vx5vN3sduzYgeLFi2t0+zUzM0O7du1w//59XL16VSlnZ2en0XLF1NQU7dq103i/n11/hoaGynE7Ojoab968QWRkJIoVKxbnfvu9fVF9/GnRooXG8b9SpUrf3TYAKOviW8di9XMx11tCrV27FuXKlUOmTJk09lVvb29ERUXh8OHDAJJmP4xpx44dsLW1RePGjZVl6dOnR+fOnfHx40ccOnRIo/yPHvMS8zu2b98+hIeHo2vXrhpjTrZt2xYWFhZa3T/NzMw0ZjB1cXGBlZUV8ufPr9HCW/3/uGL93nlHXBK6zoCv+72Pjw86deqE5s2bI1euXBg1alRCvo44NWjQACYmJlixYoWybPfu3Xj16pXGmJRE9HPY1Y6IKJHUJ3wfP37Uem7u3Ln48OEDXrx4EecJS7p06bS6Ijx48ADZs2fXOpFUN+d/8ODBD8cae7DuXLlywcDAQBn34NatWxCReAf1Vl/UqWOIXS59+vRwdnZOcDzW1tYas5FVq1YNLi4uqFevHhYsWKCcoL558wZDhw7F6tWrNcZyAID3799rvW9CLyAtLCwSfSHzyy+/aC3LlCmTxlgy0dHRmDp1KmbNmoV79+5pjA2UJUuWH443Pur1EdcYNfnz58fu3bt/auB6Nze3OGeNS2w9EyP296y+IIs9fpIumJiYaHQnU39+zM++desW3r9/H+8sW+rt1NPTE3Xr1sXQoUMxefJkeHl5wc/PD02aNFFmWPrjjz+wZs0aVKlSBfb29qhcuTIaNGgAX1/fRMXdpEkTNG/eHA8fPsSmTZswbty4OMupEzvqMbLi8v79e42L4PgkZNt9+fIlQkJC4h1rSm3YsGGoVasW8ubNi4IFC8LX1xfNmzdHoUKFvvsZgPbxyMzMDHZ2dsrxDfjaZWvs2LHYuHEj/P39cePGDZw9exZz5sxJ0Geoqcfl+/vvv3HhwgX8+uuvyJ07t8ZnqT148CDObtgxj+kFCxbEgwcPkDt3bq2uw7H366RYf0uXLsXEiRNx/fp1jYRoXOvze/tifL8H6ti/l4ROSFJJ/dyPzGp369YtXLp0SWufVlPvq0m1H6o9ePAAefLk0ZpUJL7f8h895iXmdyy+3wojIyM4OztrxZQjRw6t7dHS0lKrO7064RhXrN8774hLQteZ2sKFC5ErVy7cunULx48f/6kbOVZWVqhRowZWrlyJ4cOHA/ianLa3t0eFChV++H2JSBMTT0REiWRpaQk7OztcvnxZ6zn1xUZ8J1gxW4wkVlyDMQPQGgA5Me8RHR0NlUqFnTt3xjkDTFytupJaxYoVAQCHDx9WEk8NGjTA8ePH0atXL7i7u8PMzAzR0dHw9fWN885+Qk868+XLhwsXLiA8PDzBMxLFNzOOxBhLatSoURg4cCBatWqF4cOHI3PmzDAwMEDXrl1/Kl59k9h6JkZCvuf4fGvfSMjMRgkpEx0djaxZs2rcFY9JfcGkUqmwbt06nDx5Elu3bsXu3bvRqlUrTJw4ESdPnoSZmRmyZs2KCxcuYPfu3di5cyd27tyJxYsXw9/fX2sg4m+pWbMmjI2N0aJFC3z58kVr1rWYsQPA+PHjtcbzUUvovp6U2+5vv/2GO3fuYPPmzdizZw8WLFiAyZMnY86cOWjTpk2SfIarqys8PDywfPly+Pv7Y/ny5TAyMor3u4qPsbEx6tSpg6VLl+Lu3btxDnKtKz+7/pYvX46WLVvCz88PvXr1QtasWWFoaIjRo0fjzp07WuV/Zl9MCHWrqEuXLsHPzy/OMpcuXQKABN3YiGsSgEqVKqF3795xls+bNy8AJNl++KN+9Hv+kd+xn41JF8fnmBK6ztQOHjyoDML/77//olSpUt/9jG/x9/fH2rVrcfz4cbi5uWHLli34448/fvh8jYi0MfFERPQDqlWrhgULFuDUqVOJHvg0NkdHR+zbtw8fPnzQaPV0/fp15Xngf3dDY89E860WUbdu3dK4o3379m1ER0crAwLnypULIoKcOXNqndjFjlH9fjHvAEZERODevXsoXLhwAmoat8jISAD/a0H29u1bBAUFYejQoRg0aJBGXX5WjRo1cOLECaxfv16jO8TPWrduHcqXL4+FCxdqLH/37p0yQPP3JOTkXE29Pm7cuKH13PXr12Ftbf3DrZ2+5WfqmZj6JVamTJm09gvg676RmBZ535IrVy7s27cPZcqUSVDypWTJkihZsiRGjhyJlStXomnTpli9erWSUDEyMkKNGjVQo0YNREdH448//sDcuXMxcOBA5M6dO0ExZciQAX5+fli+fDmqVKkS7zpQD8JuYWERZ0u2mJJiPdnY2MDCwiLO5HxsmTNnRkBAAAICAvDx40f89ttvGDJkSIIST7du3UL58uWVvz9+/Ihnz56hatWqGuX8/f3RvXt3PHv2DCtXrkS1atUS1LortiZNmmDRokUwMDDQ6I4Um6OjY7z7pvp59b+XL1+GiGh877Ffm5j1F5d169bB2dkZGzZs0PichA6uHlvM34PY4qp3bGXKlIGVlRVWrlyJ/v37x5nUUA9ErZ5VDoh7Pw8PD8ezZ880luXKlQsfP35M0Hf1vf0wscflS5cuITo6WiNhEXu9/6zE/I7F/K2IeSwMDw/HvXv3fmh7+p7vnXfEJTHr7NmzZ+jUqRMqV66sTNLh4+Pz3e/3W+vS19cXNjY2WLFiBUqUKIGwsDA0b978u7EQUcIxjUtE9AN69+4NU1NTtGrVCi9evNB6PjF3hqtWrYqoqCjMmDFDY/nkyZOhUqlQpUoVAF8vOqytrTXGOgC+TpMcn9hjTE2fPh0AlPesU6cODA0NMXToUK2YRQSvX78GABQrVgw2NjaYM2eOMhU68HXK4rgu+BNj69atAKAkr9QXIbHjUc9E9DN+//132NnZoUePHrh586bW88HBwRgxYkSi39fQ0FAr3rVr1+LJkycJfg91oigh36ednR3c3d2xdOlSjfKXL1/Gnj17tC68k8rP1DMx9UusXLly4eTJkxrb5rZt2/Do0aMk+4wGDRogKipK6YoRU2RkpFKvt2/fan1H6lYq6jv06v1KzcDAQOlepi6TUD179sTgwYMxcODAeMt4eHggV65cmDBhQpxdhF++fKn8PynWk4GBAfz8/LB161acOXNG63n19xP7ezAzM0Pu3LkT/B3MmzdPo9vY7NmzERkZqRzf1Bo3bgyVSoUuXbrg7t27PzxuS/ny5TF8+HDMmDEDtra28ZarWrUqTp06hRMnTijLQkNDMW/ePDg5OSktfqpWrYqnT58q09UDX2f3mjdvnsb7JWb9xSWuY+o///yjEV9ixDz+xOz6vHfvXmX8qm8xNTVF7969cePGDWWGsZi2b9+OJUuWoEaNGnBzc1OW58qVS+v3b968eVotnho0aIATJ05g9+7dWu/97t075WZHQvbDxOwPVatWxfPnz/H3338ryyIjIzF9+nSYmZnB09Pzu++REIn5HfP29oaRkRGmTZumsf4XLlyI9+/fo1q1akkSU0zfO++IS0LXGfB1fKro6GgsXLgQ8+bNQ7p06dC6devvnndlzJgx3vWYLl06NG7cGGvWrMGSJUvg5uaW4C6/RJQwbPFERPQD8uTJg5UrV6Jx48ZwcXFB06ZNUbhwYYgI7t27h5UrV8LAwCBBU0vXqFED5cuXR//+/XH//n0ULlwYe/bswebNm9G1a1eNKePbtGmDMWPGoE2bNihWrBgOHz4c54mn2r1791CzZk34+vrixIkTWL58OZo0aaIkeXLlyoURI0agX79+uH//Pvz8/GBubo579+5h48aNaNeuHXr27In06dNjxIgRaN++PSpUqICGDRvi3r17WLx4caJalDx58gTLly8H8PWO68WLFzF37lxYW1sr3ewsLCzw22+/Ydy4cYiIiIC9vT327NmDe/fuJfhz4pMpUyZs3LgRVatWhbu7O5o1awYPDw8AXwctX7Vq1Q812a9evTqGDRuGgIAAlC5dGv/++y9WrFiRqO/G3d0dhoaGGDt2LN6/fw9jY2NUqFAh3jFOxo8fjypVqqBUqVJo3bo1Pn36hOnTp8PS0lJn3YB+pp7q77lz587w8fGBoaHhN1uNJEabNm2wbt06+Pr6okGDBrhz5w6WL1+use/8LE9PT7Rv3x6jR4/GhQsXULlyZaRPnx63bt3C2rVrMXXqVNSrVw9Lly7FrFmzULt2beTKlQsfPnzA/PnzYWFhoSQE27Rpgzdv3qBChQrIkSMHHjx4gOnTp8Pd3T3eqdrjU7hw4e+2ODQwMMCCBQtQpUoVFChQAAEBAbC3t8eTJ09w4MABWFhYKAlg9Xrq378/GjVqhPTp06NGjRqJbkE3atQo7NmzB56enmjXrh3y58+PZ8+eYe3atTh69CisrKzg6uoKLy8veHh4IHPmzDhz5gzWrVuHwMDABH1GeHg4KlasiAYNGuDGjRuYNWsWypYti5o1a2qUs7Gxga+vL9auXQsrK6sfvtg2MDDAgAEDvluub9++WLVqFapUqYLOnTsjc+bMWLp0Ke7du4f169crrWHatm2LGTNmwN/fH2fPnoWdnR2WLVsGU1NTrc9N6PqLS/Xq1bFhwwbUrl0b1apVw7179zBnzhy4urrGmchKiNGjR6NatWooW7YsWrVqhTdv3mD69OkoUKBAgt6zd+/euHDhAsaOHYsTJ06gbt26yJAhA44ePYrly5ejQIECWLJkicZr2rRpg99//x1169ZFpUqVcPHiRezevVurpV+vXr2wZcsWVK9eHS1btoSHhwdCQ0Px77//Yt26dbh//z6sra0TtB8m5rjcrl07zJ07Fy1btsTZs2fh5OSEdevW4dixY5gyZUqiJ7aIT2J+x2xsbNCvXz8MHToUvr6+qFmzprKv/PrrrzoZPPt75x1xSeg6W7x4sZKYVJ9fTZ8+Hc2aNcPs2bPxxx9/xPsZHh4emD17NkaMGIHcuXMja9asGi24/f39MW3aNBw4cABjx45Nui+EiL5K1jn0iIjSmNu3b0uHDh0kd+7cYmJiIhkyZJB8+fLJ77//LhcuXNAoG3Nq7tg+fPgg3bp1k+zZs0v69OklT548Mn78eI2ps0W+Tp/dunVrsbS0FHNzc2nQoIEEBwdrTTOtntb46tWrUq9ePTE3N5dMmTJJYGCgfPr0Sevz169fL2XLlpWMGTNKxowZJV++fNKxY0e5ceOGRrlZs2ZJzpw5xdjYWIoVKyaHDx/WmlI6Po6OjgJAeRgYGEjWrFmlcePGcvv2bY2yjx8/ltq1a4uVlZVYWlpK/fr15enTp/HWUz1tfEI9ffpUunXrJnnz5hUTExMxNTUVDw8PGTlypLx//14j5rime49d58+fP0uPHj3Ezs5OMmTIIGXKlJETJ05olTtw4IAAkLVr18YZ1/z588XZ2VkMDQ3jncI7pn379kmZMmUkQ4YMYmFhITVq1JCrV69qlPneZyambELrqZ4uPOZU25GRkdKpUyexsbERlUol6lOQuKajV4u9vtVTZJ8+fVqr7MSJE8Xe3l6MjY2lTJkycubMmQTFFd9+qd62Yps3b554eHhIhgwZxNzcXNzc3KR3797y9OlTERE5d+6cNG7cWH755RcxNjaWrFmzSvXq1eXMmTPKe6xbt04qV64sWbNmFSMjI/nll1+kffv28uzZM63Pi+s76dix4zfLxLdfnD9/XurUqSNZsmQRY2NjcXR0lAYNGkhQUJBGueHDh4u9vb0YGBhoTEn+rc+Ova5ERB48eCD+/v5iY2MjxsbG4uzsLB07dpQvX76IiMiIESOkePHiYmVlpRw7R44cKeHh4d+sn3o7OHTokLRr104yZcokZmZm0rRpU3n9+nWcr1mzZo0AkHbt2n3zvWP61jFbLb7t986dO1KvXj2xsrISExMTKV68uGzbtk3r9Q8ePJCaNWuKqampWFtbS5cuXWTXrl1x7v8JXX+xRUdHy6hRo8TR0VGMjY2lSJEism3bNmnRooU4Ojp+ty4ica/f9evXS/78+cXY2FhcXV1lw4YNWu/5vbiWLFkiZcqUEXNzc+W3wdvbW9lGYoqKipI+ffqItbW1mJqaio+Pj9y+fVscHR21pr3/8OGD9OvXT3Lnzi1GRkZibW0tpUuXlgkTJijbV0L3w/iOy3H99r148UICAgLE2tpajIyMxM3NTeN4I5L47zk+Cf0dExGZMWOG5MuXT9KnTy/ZsmWTDh06yNu3bzXKeHp6SoECBbQ+J77fwdjHg8Scd/zIOnv06JFYWlpKjRo1tGKpXbu2ZMyYUe7evSsi/ztGqI9dIiLPnz+XatWqKdtaXOctBQoUEAMDA3n8+LHWc0T0c1QiSTRSIBER6Y0hQ4Zg6NChePnyZYLHGCIiSqs2b94MPz8/HD58WJm2nvRLREQEatSogaCgIGzduvWHZ5ejlJEWzjuKFCmCzJkzIygoKKVDIUpzOMYTEREREaVp8+fPh7OzM8qWLZvSoVA80qdPj/Xr18Pd3R3169fHuXPnUjok+g85c+YMLly4AH9//5QOhShN4hhPRERERJQmrV69GpcuXcL27dsxdepUnc6uSD8vY8aMOH36dEqHQf8hly9fxtmzZzFx4kTY2dmhYcOGKR0SUZrExBMRERERpUmNGzeGmZkZWrdu/c2Bh4nov2ndunUYNmwYXFxcsGrVKpiYmKR0SERpEsd4IiIiIiIiIiIineAYT0REREREREREpBNMPBERERERERERkU5wjCfSEB0djadPn8Lc3JwDcBIRERERERFRnEQEHz58QPbs2WFgEH+7JiaeSMPTp0/h4OCQ0mEQERERERERUSrw6NEj5MiRI97nmXgiDebm5gC+bjgWFhYpHA0RERERERER6aOQkBA4ODgoeYT4MPFEGtTd6ywsLJh4IiIiIiIiIqJv+t4wPRxcnIiIiIiIiIiIdIKJJyIiIiIiIiIi0gkmnoiIiIiIiIiISCc4xhMRERERERERpVpRUVGIiIhI6TDSnPTp08PQ0PCn34eJJyIiIiIiIiJKdUQEz58/x7t371I6lDTLysoKtra23x1A/FuYeCIiIiIiIiKiVEeddMqaNStMTU1/KjlCmkQEYWFhCA4OBgDY2dn98Hsx8UREREREREREqUpUVJSSdMqSJUtKh5MmZciQAQAQHByMrFmz/nC3Ow4uTkRERERERESpinpMJ1NT0xSOJG1Tf78/M4YWE09ERERERERElCqxe51uJcX3y8QTERERERERERHpBBNPREREREREREQpTKVSYdOmTSkdRpLj4OJERERERESpnFPf7Qkue39MNR1GQpS6tWzZEkuXLkX79u0xZ84cjec6duyIWbNmoUWLFliyZMl33+vgwYMoX7483r59Cysrq++Wf/bsGTJlyvSDkesvtngiIiIiIiIiIvp/Dg4OWL16NT59+qQs+/z5M1auXIlffvklyT8vPDwcAGBrawtjY+Mkf/+UxsQTEREREREREdH/K1q0KBwcHLBhwwZl2YYNG/DLL7+gSJEiyrLo6GiMHj0aOXPmRIYMGVC4cGGsW7cOAHD//n2UL18eAJApUyaoVCq0bNkSAODl5YXAwEB07doV1tbW8PHxAaDd1e7x48do3LgxMmfOjIwZM6JYsWL4559/AAAXL15E+fLlYW5uDgsLC3h4eODMmTO6/Fp+GLvaERERERERERHF0KpVKyxevBhNmzYFACxatAgBAQE4ePCgUmb06NFYvnw55syZgzx58uDw4cNo1qwZbGxsULZsWaxfvx5169bFjRs3YGFhgQwZMiivXbp0KTp06IBjx47F+fkfP36Ep6cn7O3tsWXLFtja2uLcuXOIjo4GADRt2hRFihTB7NmzYWhoiAsXLiB9+vS6+0J+AhNPRESUYBw/gohIE4+LRESa0spxsVmzZujXrx8ePHgAADh27BhWr16tJJ6+fPmCUaNGYd++fShVqhQAwNnZGUePHsXcuXPh6emJzJkzAwCyZs2qNcZTnjx5MG7cuHg/f+XKlXj58iVOnz6tvE/u3LmV5x8+fIhevXohX758yvvpKyaeiIiIiIiIiIhisLGxQbVq1bBkyRKICKpVqwZra2vl+du3byMsLAyVKlXSeF14eLhGd7z4eHh4fPP5CxcuoEiRIkrSKbbu3bujTZs2WLZsGby9vVG/fn3kypUrATVLfkw8ERERERERERHF0qpVKwQGBgIAZs6cqfHcx48fAQDbt2+Hvb29xnMJGSA8Y8aM33w+Zre8uAwZMgRNmjTB9u3bsXPnTgwePBirV69G7dq1v/vZyY2DixMRERERERERxeLr64vw8HBEREQoA4Crubq6wtjYGA8fPkTu3Lk1Hg4ODgAAIyMjAEBUVFSiP7tQoUK4cOEC3rx5E2+ZvHnzolu3btizZw/q1KmDxYsXJ/pzkgNbPBEREREREZHeSitjBlHqY2hoiGvXrin/j8nc3Bw9e/ZEt27dEB0djbJly+L9+/c4duwYLCws0KJFCzg6OkKlUmHbtm2oWrUqMmTIADMzswR9duPGjTFq1Cj4+flh9OjRsLOzw/nz55E9e3a4u7ujV69eqFevHnLmzInHjx/j9OnTqFu3bpJ/B0mBLZ6IiIiIiIiIiOJgYWEBCwuLOJ8bPnw4Bg4ciNGjRyN//vzw9fXF9u3bkTNnTgCAvb09hg4dir59+yJbtmxKt72EMDIywp49e5A1a1ZUrVoVbm5uGDNmDAwNDWFoaIjXr1/D398fefPmRYMGDVClShUMHTo0Seqc1NjiiYiIiIiIiIgIwJIlS775/KZNm5T/q1QqdOnSBV26dIm3/MCBAzFw4ECNZeqZ8WITEY2/HR0dsW7dujjLrlq16ptx6hO2eCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJ5h4IiIiIiIiIiIinWDiiYiIiIiIiIiIdCJdSgdARERERERERJRUnPpuT9bPuz+mWrJ+XmrDxBMREREREf1nJOaClBeTREQ/j13tiIiIiIiIiIiSiZeXFzp16oSuXbsiU6ZMyJYtG+bPn4/Q0FAEBATA3NwcuXPnxs6dO5XXXL58GVWqVIGZmRmyZcuG5s2b49WrV8rzu3btQtmyZWFlZYUsWbKgevXquHPnjvL8/fv3oVKpsGHDBpQvXx6mpqYoXLgwTpw4ofP6MvFERERERERERJSMli5dCmtra5w6dQqdOnVChw4dUL9+fZQuXRrnzp1D5cqV0bx5c4SFheHdu3eoUKECihQpgjNnzmDXrl148eIFGjRooLxfaGgounfvjjNnziAoKAgGBgaoXbs2oqOjNT63f//+6NmzJy5cuIC8efOicePGiIyM1Gld2dWOiIiIiIiIiCgZFS5cGAMGDAAA9OvXD2PGjIG1tTXatm0LABg0aBBmz56NS5cuYd++fShSpAhGjRqlvH7RokVwcHDAzZs3kTdvXtStW1fj/RctWgQbGxtcvXoVBQsWVJb37NkT1ap97UY8dOhQFChQALdv30a+fPl0Vle2eCIiIiIiIiIiSkaFChVS/m9oaIgsWbLAzc1NWZYtWzYAQHBwMC5evIgDBw7AzMxMeagTRerudLdu3ULjxo3h7OwMCwsLODk5AQAePnwY7+fa2dkpn6FLbPFERERERERERJSM0qdPr/G3SqXSWKZSqQAA0dHR+PjxI2rUqIGxY8dqvY86eVSjRg04Ojpi/vz5yJ49O6Kjo1GwYEGEh4fH+7kxP0OXmHgiIiIiIiIiItJTRYsWxfr16+Hk5IR06bTTOK9fv8aNGzcwf/58lCtXDgBw9OjR5A4zXuxqR0RERERERESkpzp27Ig3b96gcePGOH36NO7cuYPdu3cjICAAUVFRyJQpE7JkyYJ58+bh9u3b2L9/P7p3757SYSuYeNJzM2fOhJOTE0xMTFCiRAmcOnXqm+WnTJkCFxcXZMiQAQ4ODujWrRs+f/6cTNESERERERERUVLKnj07jh07hqioKFSuXBlubm7o2rUrrKysYGBgAAMDA6xevRpnz55FwYIF0a1bN4wfPz6lw1awq50e+/vvv9G9e3fMmTMHJUqUwJQpU+Dj44MbN24ga9asWuVXrlyJvn37YtGiRShdujRu3ryJli1bQqVSYdKkSSlQAyIiIiIiIqLkdX9MtZQO4ZsOHjyotez+/ftay0RE+X+ePHmwYcOGeN/T29sbV69ejff1Tk5OGn8DgJWVldYyXWDiSY9NmjQJbdu2RUBAAABgzpw52L59OxYtWoS+fftqlT9+/DjKlCmDJk2aAPi6YTVu3Bj//PNPssZNREREFJtT3+0JLqvvFwxERESUcOxqp6fCw8Nx9uxZeHt7K8sMDAzg7e2NEydOxPma0qVL4+zZs0p3vLt372LHjh2oWrVqssRMRERERERERBQTWzzpqVevXiEqKgrZsmXTWJ4tWzZcv349ztc0adIEr169QtmyZSEiiIyMxO+//44///wz3s/58uULvnz5ovwdEhKSNBUgIiIiIiIiov88tnhKQw4ePIhRo0Zh1qxZOHfuHDZs2IDt27dj+PDh8b5m9OjRsLS0VB4ODg7JGDERERERERERpWVs8aSnrK2tYWhoiBcvXmgsf/HiBWxtbeN8zcCBA9G8eXO0adMGAODm5obQ0FC0a9cO/fv3h4GBdp6xX79+GtMshoSEMPlEREREREREREmCLZ70lJGRETw8PBAUFKQsi46ORlBQEEqVKhXna8LCwrSSS4aGhgAQ70j1xsbGsLCw0HgQERERERERESUFtnjSY927d0eLFi1QrFgxFC9eHFOmTEFoaKgyy52/vz/s7e0xevRoAECNGjUwadIkFClSBCVKlMDt27cxcOBA1KhRQ0lAERERERERERElFyae9FjDhg3x8uVLDBo0CM+fP4e7uzt27dqlDDj+8OFDjRZOAwYMgEqlwoABA/DkyRPY2NigRo0aGDlyZEpVgYiIiIiIiIj+w5h40nOBgYEIDAyM87mDBw9q/J0uXToMHjwYgwcPTobIiIiIiIiIiIi+jWM8ERERERERERGlIvfv34dKpcKFCxdSOpTvYosnIiIiIiIiIko7hlgm8+e9T7K3atmyJd69e4dNmzYl2XumNLZ4IiIiIiIiIiIinWDiiYiIiIiIiIgoGa1btw5ubm7IkCEDsmTJAm9vb/Tq1QtLly7F5s2boVKpoFKplLGdT506hSJFisDExATFihXD+fPnU7YCicCudkREREREREREyeTZs2do3Lgxxo0bh9q1a+PDhw84cuQI/P398fDhQ4SEhGDx4sUAgMyZM+Pjx4+oXr06KlWqhOXLl+PevXvo0qVLCtci4Zh4IiIiIiIiIiJKJs+ePUNkZCTq1KkDR0dHAICbmxsAIEOGDPjy5QtsbW2V8kuWLEF0dDQWLlwIExMTFChQAI8fP0aHDh1SJP7EYlc7IiIiIiIiIqJkUrhwYVSsWBFubm6oX78+5s+fj7dv38Zb/tq1ayhUqBBMTEyUZaVKlUqOUJMEE09ERERERERERMnE0NAQe/fuxc6dO+Hq6orp06fDxcUF9+7dS+nQdIJd7YiIiAA49d2eoHL3x1TTcSRERERElNapVCqUKVMGZcqUwaBBg+Do6IiNGzfCyMgIUVFRGmXz58+PZcuW4fPnz0qrp5MnT6ZE2D+ELZ6IiIiIiIiIiJLJP//8g1GjRuHMmTN4+PAhNmzYgJcvXyJ//vxwcnLCpUuXcOPGDbx69QoRERFo0qQJVCoV2rZti6tXr2LHjh2YMGFCSlcjwdjiiYiIiIiItLAlKBGRblhYWODw4cOYMmUKQkJC4OjoiIkTJ6JKlSooVqwYDh48iGLFiuHjx484cOAAvLy8sHXrVvz+++8oUqQIXF1dMXbsWNStWzelq5IgTDwRERERERERUdox5H1KR/BN+fPnx65du+J8zsbGBnv27NFaXrJkSVy4cEFjmYjoIrwkx8QTEZEOJPQuMcA7xURERERElHZxjCciIiIiIiIiItIJJp6IiIiIiIiIiEgn2NWOiIiIiIiIKJlxaAb6r2CLJyIiIiIiIiJKlVLLANupVVJ8v0w8EREREREREVGqkj59egBAWFhYCkeStqm/X/X3/SPY1Y6IiIiIiIiIUhVDQ0NYWVkhODgYAGBqagqVSpXCUaUdIoKwsDAEBwfDysoKhoaGP/xeTDwRERERERERUapja2sLAEryiZKelZWV8j3/KCaeiIiIiIiIiCjVUalUsLOzQ9asWREREZHS4aQ56dOn/6mWTmpMPBERERERERFRqmVoaJgkCZLv4UyEP4aDixMRERERERERkU4w8URERERERERERDrBxBMREREREREREekEE09ERERERERERKQTTDwREREREREREZFOMPFEREREREREREQ6wcQTERERERERERHpBBNPRERERERERESkE0w8ERERERERERGRTjDxREREREREREREOsHEExERERERERER6QQTT0REREREREREpBNMPBERERERERERkU4w8URERERERERERDrBxBMREREREREREekEE09ERERERERERKQTTDwREREREREREZFOMPFEREREREREREQ6wcQTERERERERERHpBBNPRERERERERESkE0w8ERERERERERGRTjDxREREREREREREOsHEExERERERERER6QQTT0REREREREREpBNMPBERERERERERkU4w8URERERERERERDrBxJOemzlzJpycnGBiYoISJUrg1KlT3yz/7t07dOzYEXZ2djA2NkbevHmxY8eOZIqWiIiIiIiIiOh/0qV0ABS/v//+G927d8ecOXNQokQJTJkyBT4+Prhx4wayZs2qVT48PByVKlVC1qxZsW7dOtjb2+PBgwewsrJK/uCJiIiIiIiI6D+PiSc9NmnSJLRt2xYBAQEAgDlz5mD79u1YtGgR+vbtq1V+0aJFePPmDY4fP4706dMDAJycnJIzZCIiIiIiIiIiBbva6anw8HCcPXsW3t7eyjIDAwN4e3vjxIkTcb5my5YtKFWqFDp27Ihs2bKhYMGCGDVqFKKiouL9nC9fviAkJETjQURERERERESUFJh40lOvXr1CVFQUsmXLprE8W7ZseP78eZyvuXv3LtatW4eoqCjs2LEDAwcOxMSJEzFixIh4P2f06NGwtLRUHg4ODklaDyIiIiIiIiL672LiKQ2Jjo5G1qxZMW/ePHh4eKBhw4bo378/5syZE+9r+vXrh/fv3yuPR48eJWPERERERERERJSWcYwnPWVtbQ1DQ0O8ePFCY/mLFy9ga2sb52vs7OyQPn16GBoaKsvy58+P58+fIzw8HEZGRlqvMTY2hrGxcdIGT0REREREREQEtnjSW0ZGRvDw8EBQUJCyLDo6GkFBQShVqlScrylTpgxu376N6OhoZdnNmzdhZ2cXZ9KJiIiIiIiIiEiXmHjSY927d8f8+fOxdOlSXLt2DR06dEBoaKgyy52/vz/69eunlO/QoQPevHmDLl264ObNm9i+fTtGjRqFjh07plQViIiIiIiIiOg/jF3t9FjDhg3x8uVLDBo0CM+fP4e7uzt27dqlDDj+8OFDGBj8L3fo4OCA3bt3o1u3bihUqBDs7e3RpUsX9OnTJ6WqQERERERERET/YUw86bnAwEAEBgbG+dzBgwe1lpUqVQonT57UcVRERERERERERN/HrnZERERERERERKQTTDwREREREREREZFOMPFEREREREREREQ6wcQTERERERERERHpBBNPRERERERERESkE0w8ERERERERERGRTjDxREREREREREREOsHEUxJ69OgRHj9+rPx96tQpdO3aFfPmzUvBqIiIiIiIiIiIUgYTT0moSZMmOHDgAADg+fPnqFSpEk6dOoX+/ftj2LBhKRwdEREREREREVHyYuIpCV2+fBnFixcHAKxZswYFCxbE8ePHsWLFCixZsiRlgyMiIiIiIiIiSmZMPCWhiIgIGBsbAwD27duHmjVrAgDy5cuHZ8+epWRoRERERERERETJjomnJFSgQAHMmTMHR44cwd69e+Hr6wsAePr0KbJkyZLC0RERERERERERJa90KR1AWjJ27FjUrl0b48ePR4sWLVC4cGEAwJYtW5QueERERPTznPpuT3DZ+2Oq6TASIiIiIvoWJp6SkJeXF169eoWQkBBkypRJWd6uXTuYmpqmYGRERERERERERMmPiackJiI4e/Ys7ty5gyZNmsDc3BxGRkZMPNFP4Z19IiIiIiIiSo2YeEpCDx48gK+vLx4+fIgvX76gUqVKMDc3x9ixY/HlyxfMmTMnpUMkIiIiIiIiIko2HFw8CXXp0gXFihXD27dvkSFDBmV57dq1ERQUlIKRERERERERERElP7Z4SkJHjhzB8ePHYWRkpLHcyckJT548SaGoiIiIiIiIiIhSBls8JaHo6GhERUVpLX/8+DHMzc1TICIiIiIiIiIiopTDxFMSqly5MqZMmaL8rVKp8PHjRwwePBhVq1ZNucCIiIiIiIiIiFIAu9oloYkTJ8LHxweurq74/PkzmjRpglu3bsHa2hqrVq1K6fCIiIiIiIiIiJIVE09JKEeOHLh48SJWr16NS5cu4ePHj2jdujWaNm2qMdg4EREREREREdF/ARNPSSxdunRo1qxZSodBRERERERERJTimHhKQn/99dc3n/f390+mSIiIiIiIiIiIUh4TT0moS5cuGn9HREQgLCwMRkZGMDU1ZeKJiIiSnVPf7Qkue39MNR1GQkRERET/RZzVLgm9fftW4/Hx40fcuHEDZcuW5eDiRERERERERPSfw8STjuXJkwdjxozRag1FRERERERERJTWMfGUDNKlS4enT5+mdBhERERERERERMmKYzwloS1btmj8LSJ49uwZZsyYgTJlyqRQVEREREREREREKYOJpyTk5+en8bdKpYKNjQ0qVKiAiRMnpkxQREREREREREQphImnJBQdHZ3SIRARERERERER6Q2O8URERERERERERDrBFk8/qXv37gkuO2nSJB1GQkRERERERESkX5h4+knnz59PUDmVSqXjSIiIiIiIiIiI9AsTTz/pwIEDKR0CEREREREREZFe4hhPRERERERERESkE2zxlMTOnDmDNWvW4OHDhwgPD9d4bsOGDSkUFRERERERERFR8mOLpyS0evVqlC5dGteuXcPGjRsRERGBK1euYP/+/bC0tEzp8IiIiIiIiIiIkhUTT0lo1KhRmDx5MrZu3QojIyNMnToV169fR4MGDfDLL7+kdHhERERERERERMmKiackdOfOHVSrVg0AYGRkhNDQUKhUKnTr1g3z5s1L4eiIiIiIiIiIiJIXE09JKFOmTPjw4QMAwN7eHpcvXwYAvHv3DmFhYSkZGhERERERERFRsmPiKQmoE0y//fYb9u7dCwCoX78+unTpgrZt26Jx48aoWLFiSoZIRERERERERJTsOKtdEihUqBB+/fVX+Pn5oX79+gCA/v37I3369Dh+/Djq1q2LAQMGpHCURKnckEQM0D/kve7iICIiIiIiogRj4ikJHDp0CIsXL8bo0aMxcuRI1K1bF23atEHfvn1TOjQiIiIiIiIiohTDrnZJoFy5cli0aBGePXuG6dOn4/79+/D09ETevHkxduxYPH/+PKVDJCIiIiIiIiJKdkw8JaGMGTMiICAAhw4dws2bN1G/fn3MnDkTv/zyC2rWrJnS4RERERERERERJSsmnnQkd+7c+PPPPzFgwACYm5tj+/btKR0SEREREREREVGyYuJJBw4fPoyWLVvC1tYWvXr1Qp06dXDs2LEfeq+ZM2fCyckJJiYmKFGiBE6dOpWg161evRoqlQp+fn4/9LlERERERERERD+Liack8vTpU4waNQp58+aFl5cXbt++jWnTpuHp06eYP38+SpYsmej3/Pvvv9G9e3cMHjwY586dQ+HCheHj44Pg4OBvvu7+/fvo2bMnypUr96PVISIiIiIiIiL6aUw8JYEqVarA0dER06dPR+3atXHt2jUcPXoUAQEByJgx4w+/76RJk9C2bVsEBATA1dUVc+bMgampKRYtWhTva6KiotC0aVMMHToUzs7OP/zZREREREREREQ/i4mnJJA+fXqsW7cOjx8/xtixY+Hi4vLT7xkeHo6zZ8/C29tbWWZgYABvb2+cOHEi3tcNGzYMWbNmRevWrX86BiIiIiIiIiKin5EupQNIC7Zs2ZLk7/nq1StERUUhW7ZsGsuzZcuG69evx/mao0ePYuHChbhw4UKCP+fLly/48uWL8ndISMgPxUtEREREREREFBtbPKURHz58QPPmzTF//nxYW1sn+HWjR4+GpaWl8nBwcNBhlERERERERET0X8IWT3rK2toahoaGePHihcbyFy9ewNbWVqv8nTt3cP/+fdSoUUNZFh0dDQBIly4dbty4gVy5cmm9rl+/fujevbvyd0hICJNPRERERERERJQkmHjSU0ZGRvDw8EBQUBD8/PwAfE0kBQUFITAwUKt8vnz58O+//2osGzBgAD58+ICpU6fGm0wyNjaGsbFxksdPRERERERERMTEkx7r3r07WrRogWLFiqF48eKYMmUKQkNDERAQAADw9/eHvb09Ro8eDRMTExQsWFDj9VZWVgCgtZyIiIiIiIiIKDkw8aTHGjZsiJcvX2LQoEF4/vw53N3dsWvXLmXA8YcPH8LAgMN0EREREREREZF+YuJJzwUGBsbZtQ4ADh48+M3XLlmyJOkDIkpiTn23J6jcfRMdB0JERERERERJjs1liIiIiIiIiIhIJ5h4IiIiIiIiIiIinWDiiYiIiIiIiIiIdIKJJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgn0qV0AERJyanv9gSXvT+mmg4jISIiIiIiIiK2eCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnOLg4UVozxDIRZd/rLg4iIiIiIiL6z2OLJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSiXQpHQBRihlimYiy73UXBxEREVFqxnMqIiL6BrZ4IiIiIiIiIiIinWDiiYiIiIiIiIiIdIKJJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp3grHZEREREREREREkpoTN+/gdm+2SLJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJ5h4IiIiIiIiIiIinUiX0gEQERGRnhhimYiy73UXBxG3RSIiojSDLZ6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJzirnZ6bOXMmxo8fj+fPn6Nw4cKYPn06ihcvHmfZ+fPn46+//sLly5cBAB4eHhg1alS85YmIiIiIiNKUhM6KyRkxiZINWzzpsb///hvdu3fH4MGDce7cORQuXBg+Pj4IDg6Os/zBgwfRuHFjHDhwACdOnICDgwMqV66MJ0+eJHPkRERERERERERs8aTXJk2ahLZt2yIgIAAAMGfOHGzfvh2LFi1C3759tcqvWLFC4+8FCxZg/fr1CAoKgr+/f7LETERERETxSGhLDICtMYiIKM1g4klPhYeH4+zZs+jXr5+yzMDAAN7e3jhx4kSC3iMsLAwRERHInDlzvGW+fPmCL1++KH+HhIT8eNBERERERESU9Ji4plSMXe301KtXrxAVFYVs2bJpLM+WLRueP3+eoPfo06cPsmfPDm9v73jLjB49GpaWlsrDwcHhp+ImIiIiIiIiIlJj4imNGjNmDFavXo2NGzfCxMQk3nL9+vXD+/fvlcejR4+SMUoiIiIiIiIiSsvY1U5PWVtbw9DQEC9evNBY/uLFC9ja2n7ztRMmTMCYMWOwb98+FCpU6JtljY2NYWxs/NPxEhERUdJw6rs9QeXuj6mm40iIiIiIfh5bPOkpIyMjeHh4ICgoSFkWHR2NoKAglCpVKt7XjRs3DsOHD8euXbtQrFix5AiViIiIiIiIiChObPGkx7p3744WLVqgWLFiKF68OKZMmYLQ0FBlljt/f3/Y29tj9OjRAICxY8di0KBBWLlyJZycnJSxoMzMzGBmZpZi9SAiIiIiIiKi/yYmnvRYw4YN8fLlSwwaNAjPnz+Hu7s7du3apQw4/vDhQxgY/K/R2uzZsxEeHo569eppvM/gwYMxZMiQ5AydiIiIiIiIiIiJJ30XGBiIwMDAOJ87ePCgxt/379/XfUBERERERJS6DbFMRNn3uouDiP4TOMYTERERERERERHpBFs8EREREaVGbLFAREREqQATT0REKY0Xj0S6xX2MiIiIKMWwqx0REREREREREekEE09ERERERERERKQTTDwREREREREREZFOMPFEREREREREREQ6wcQTERERERERERHpBBNPRERERERERESkE+lSOgAiIiIiIiK9NMQyEWXf6y4OIqJUjC2eiIiIiIiIiIhIJ5h4IiIiIiIiIiIinWBXOyIiIiIiIiJKfuzO+p/AxBMREekGTySIiIiIiP7z2NWOiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJ5h4IiIiIiIiIiIinWDiiYiIiIiIiIiIdIKz2hERESUGZ+sjIiIiIkowtngiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJ5h4IiIiIiIiIiIinWDiiYiIiIiIiIiIdIKJJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSaeiIiIiIiIiIhIJ5h4IiIiIiIiIiIinWDiiYiIiIiIiIiIdIKJJyIiIiIiIiIi0gkmnoiIiIiIiIiISCeYeCIiIiIiIiIiIp1g4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp6IiIiIiIiIiEgnmHgiIiIiIiIiIiKdYOKJiIiIiIiIiIh0goknIiIiIiIiIiLSCSae9NzMmTPh5OQEExMTlChRAqdOnfpm+bVr1yJfvnwwMTGBm5sbduzYkUyREhERERERERFpYuJJj/3999/o3r07Bg8ejHPnzqFw4cLw8fFBcHBwnOWPHz+Oxo0bo3Xr1jh//jz8/Pzg5+eHy5cvJ3PkRERERERERERMPOm1SZMmoW3btggICICrqyvmzJkDU1NTLFq0KM7yU6dOha+vL3r16oX8+fNj+PDhKFq0KGbMmJHMkRMRERERERERMfGkt8LDw3H27Fl4e3srywwMDODt7Y0TJ07E+ZoTJ05olAcAHx+feMsTEREREREREelSupQOgOL26tUrREVFIVu2bBrLs2XLhuvXr8f5mufPn8dZ/vnz5/F+zpcvX/Dlyxfl7/fv3wMAQkJCfjT0FBX9JSzBZUNUkvA3TuHvI63WC0h43dJqvYDUVbe0Wi+A2yKQuuqWVusFcFsEUlfd0mq9AG6LQOqqW1qtF6CjuqXVegEpXre0Wi8g7W6LP0qdNxD5Tl2F9NKTJ08EgBw/flxjea9evaR48eJxviZ9+vSycuVKjWUzZ86UrFmzxvs5gwcPFgB88MEHH3zwwQcffPDBBx988MEHH4l+PHr06Jv5DbZ40lPW1tYwNDTEixcvNJa/ePECtra2cb7G1tY2UeUBoF+/fujevbvyd3R0NN68eYMsWbJApVL9RA3SjpCQEDg4OODRo0ewsLBI6XCSDOuV+qTVurFeqU9arRvrlfqk1bqxXqlPWq0b65X6pNW6sV76SUTw4cMHZM+e/ZvlmHjSU0ZGRvDw8EBQUBD8/PwAfE0KBQUFITAwMM7XlCpVCkFBQejatauybO/evShVqlS8n2NsbAxjY2ONZVZWVj8bfppkYWGRKg8G38N6pT5ptW6sV+qTVuvGeqU+abVurFfqk1brxnqlPmm1bqyX/rG0tPxuGSae9Fj37t3RokULFCtWDMWLF8eUKVMQGhqKgIAAAIC/vz/s7e0xevRoAECXLl3g6emJiRMnolq1ali9ejXOnDmDefPmpWQ1iIiIiIiIiOg/ioknPdawYUO8fPkSgwYNwvPnz+Hu7o5du3YpA4g/fPgQBgb/m5iwdOnSWLlyJQYMGIA///wTefLkwaZNm1CwYMGUqgIRERERERER/Ycx8aTnAgMD4+1ad/DgQa1l9evXR/369XUc1X+LsbExBg8erNUlMbVjvVKftFo31iv1Sat1Y71Sn7RaN9Yr9UmrdWO9Up+0WjfWK3VTiXxv3jsiIiIiIiIiIqLEM/h+ESIiIiIiIiIiosRj4omIiIiIiIiIiHSCiSciIiIiIiIiItIJJp7oPyk6OjqlQ9CZtFw3Sn3S2jCCaa0+sfH4QUQ/Iq0fG4mI6Ocw8UT/SQYGXzf9tHSRdenSJYSGhip1SysuXbqk8XdaWmdp1ZkzZ5T/q1SqNHNB8vnzZ2X7Syt1Ulu3bh0+fPiQJuunrktaPnakpfUFpL36AEBoaGhKh6BTKpUKQNpad0FBQTh37pzGsrR8HElL0vJ6Skv7WGxpcb2l5fWVWGnrCpXoOwYOHIgqVapgxYoVuHfvXppJ0syaNQvdu3eHm5sb5s+fr3Hhn5otW7YMderUQcuWLTF79myISJpZZy1atECvXr0wdepUiEia+WHatGkTqlevDn9/fwwePBjh4eHKBUlqNnr0aBQuXBjNmjXD+vXrERERkdIhJZl9+/ZhxowZKF26NHr06IFDhw6liXUGACNHjsTEiRNx8+ZNjTql9v1t7ty5mDFjBsLCwhAZGQmVSpUmTtiHDRuG06dP4/HjxykdSpIaNmwYAgICsGzZMjx48EBZntq3Q+DrsbFZs2Y4efIknj9/nmZuNuzYsQMzZ85ExYoVERgYiGXLlgH4euMytdevefPmmD59OjZs2JDSoSSp8ePHY8aMGXjy5EmaOVdUO3DgAM6ePQsgbSV5W7VqhVGjRmHTpk0AkGbWW58+fbBixQpcuHAhzZxPJYW0sXaJEiAyMhINGzaEm5sbNmzYgJIlS2L+/Pl48uRJSof209q2bYudO3eiQ4cO2LRpE1q0aIEZM2akdFg/rVq1aggKCkLmzJmxYsUK5M+fH0eOHMGnT59SOrSf5ufnBycnJ0yaNAne3t6YMGECQkJCUjqsn1a9enWcOnUK7u7u2LVrF1xdXbF27Vq8fv06pUP7Kf369cOsWbNQuHBh+Pv7IyAgAKtWrUrpsJJE+fLlcfDgQfTp0weRkZGoUKECBg0ahEePHqV0aD/t/fv3uHHjBkqVKoWuXbsqJ7ep+eL4zp07ePnyJaZNm4a6deuiXbt2ePXqVao/YX/w4AEePnyIjh07okWLFhg1ahQ+f/6c0mElicKFC8Pb2xvDhw/HH3/8gX79+gFAqr8g+fjxI4oUKYKoqCgMGTIElStXxq5duxAZGZnSof20qlWrYsOGDdi9ezfCwsIwceJE+Pn5pYmbKSVLlsSDBw/QuXNn+Pn5YdOmTQgPD0/psH5KREQEHj58iBMnTqBo0aLo06cP9uzZk9JhJYkDBw5gyJAhaNCgARo3bowNGzYgIiICKpUKUVFRKR3eT/Hx8cGdO3cwYsQIlC5dGvv378e7d+9SOqyf9vHjRxw7dgylS5dG3759sXPnzpQOST8I0X9QcHCwTJw4UTJnziytWrWSY8eOpXRIPywqKkrj72vXrsm4cePE0NBQevXqJdHR0SkU2c+LjIwUEZEvX77I06dPpV69emJjYyOzZ8+WN2/epHB0Py7mOgkJCZFu3brJb7/9JpUrV5bXr1+nYGQ/T73OoqOjJSIiQlq0aCEuLi4yZMgQefr0aQpH92Ni70OnTp2SBg0aSMmSJWX48OEpFFXSiIyMVNaZiEh4eLisXr1azM3NpWXLlvLvv/+mYHQ/LvZxcf369dKyZUtxcnKSESNGKMtT8/Hx06dPsmjRIqlYsaJkzZpVdu3aJeHh4Skd1k+7fPmy/PXXX2JhYSFVq1aVbdu2pXRIPyz29nXz5k2ZNm2a5MiRQypVqpTqj/cxXbhwQXr06CEGBgbSr18/uXv3bkqH9MO+fPmi8ferV69k79694uzsLMWLF5cnT56ISOo+fkRHR8u9e/ekWrVqUrZsWenYsaOEhoamdFg/JPbx/q+//pImTZqIk5OTTJs2LYWiSlqfP3+Wu3fvSu3atcXT01OqVKmirK/Y9U9tPn36JM+ePZNq1apJkSJFpFevXvL48eOUDuuHxD4mbN68WapXry7FixeXYcOGpVBU+oOJJ/pPiO/kYPv27VK0aFFp0KCBnD59Opmj+nnfOulZs2aNGBkZyYABA5IxoqQXu45dunSRHDlyyKJFi1L1SZ+IyPv370VEJCIiQjZs2CBly5aVokWLpsqLkdgnPjEvgAcNGiSFCxeW0aNHy8ePH5M7tCQRHR0t0dHRSj3v3bsnf/75p7i5ucm4ceNSOLofExISovx/7969Gs8FBQVJjhw5pHXr1vLhw4fkDu2nqY8NMbfLhw8fypQpUyRdunTSp0+flAotScRM8IaEhEirVq3E1NRUli9fniqPi1FRUVpx379/X8qVKyeenp7y119/pVBkP0ddp5h1Cw8Pl7Nnz0qePHmkdOnS8unTJ60yqUnsY/+yZcvE2tpaOnbsKI8ePUqhqH7c58+flf9v3rxZ3r59q/x99+5dKViwoJQuXVpZlprWW8x1pY47LCxMxo0bJyVLlpSGDRtKWFhYSoX302Kui7t378rIkSNFpVLJqFGjUjCqHxcdHa2VBP3w4YNs2rRJihUrJrlz55ZXr16JSOpLPsUX74gRI6R06dLSqlWrVH+zUv3v7du3ZeTIkZIjRw7p0aNHSoaW4ph4ojTv+PHjcu7cORERadeunUyePFnj+aCgIHFzc5Nu3bpJeHh4qjmJiBnnmjVr5OjRo1plli9fLiYmJrJq1arkDO2nJOTH848//hBra2u5f/++iKSeE7/9+/crJ+KDBg2ShQsXKgma6OhoOXDggJQrV06aNGmSqu48xvz+//rrL42Wamp9+/aVnDlzKgne1HCS9L0Ynz59Kj179pQKFSrI4cOHkymqpLFu3TqpU6eOfPr0Sbp27SpZsmSRFy9eKAk2ka/bq6GhocyePVtEUs9+FjPO58+fazz36dMnWbx4sZiYmMjUqVOTO7SfEtf3H3Mb7dKli5iZmcmZM2e0ntNnMZMTc+bM0Xju2bNnUqtWLSlfvrycOHEiuUNLMhcuXNBadvfuXXFycpK6deumQEQ/J65tK2bLyXXr1omZmZmMHz8+3vL6aOfOnZIvXz4REenWrZu4uLhoXfxev35dcubMKR06dEiJEJNE7JtbX758kfnz50vp0qVl0KBBEhERkUKRJd63tq2PHz/KjBkzxMjISBYtWpSMUSWNmDfqdu3apdHS7vLly1KyZElxdXVVkqWp5Tc6JvWNrZjrcfr06VKqVCkZO3asVuJNn8X+/mP+/fr1a5k5c6bkzJkz1Z17JCUmnijNio6OlhcvXoidnZ00adJE/P39JWPGjHGeAK5du1YMDQ1l165dKRBp4sU8QP/zzz9SokQJqVSpkly8eFGj3MePH6VHjx5SrVo1rQswfRSzXgsWLJD+/ftLo0aN5MiRI/Ly5UuNshUqVJCKFSsmd4g/7NGjR1K2bFkpV66ctG3bVgwNDZVuTDFbZyxcuFDKlSsnW7du1XhOX8VcZ48ePZL06dNLzZo140w++fr6SvHixZM9xh8Rs14rVqyQvn37yoABA2Tt2rUa5W7cuCElSpSQ7t27i4j+ry+1s2fPikqlkgIFCoilpaVcunRJRLRbCk2ZMkXs7Ozk8uXLKRZrYsRcb8uXL5d69eppHfM/fvwow4YNk1KlSsk///yT3CH+kJj1evDggdy5cyfOE/J69epJ3rx5NVqz6bP9+/dL4cKF5cCBA9KlSxdRqVRy584dEflfnZ8+fSoFCxaURo0apWSoiRJzfe3Zs0cKFSokK1as0Hp+7969kjdvXq2Emz6LWbdTp05JUFCQ/Pvvv1otZWbOnCnp06dXEqGpweXLl8XV1VUcHBzE0tJSbt68qVVG/Tvt5eUV5/mkPoq5zqZMmSJly5bVOqZ//vxZ+vbtK7/99ptcv35dRPT/9yxmva5evSo3btyQa9euaZR5//699O3bV8qWLZtq1pfI15vijo6OEh0dLT169BAXFxd58eKFRplz585J8eLF5Y8//tBI/OqzmOts/vz5UrhwYXn48KGIaG5vPXr0kPz586eabq2xt0X1I6bg4GDp1auXVK1aVa5cuZLcIeoFJp4ozbt48aJYW1tL+vTpZfXq1cry2Aexnj17Srly5fR+7KCYcQ8fPlyaN28u+fPnl/Tp04uvr69Wl8EDBw5Ivnz55OTJk8kd6g/r1auX2NraSpcuXaRu3bpia2srAwcOlE+fPikH9yNHjkiFChWU1mypwe7du8XW1laMjY2VJKf6zqJ6vYaHh0u1atWkdu3aKRZnQsXcFocOHSpNmzaVvHnzikqlkkqVKmkln27fvi1lypSRnTt3pki8P6JXr15iZ2cnrVq1En9/f7GyspKhQ4eKyP/qv2vXLjEzM5OzZ8+mZKgJEh0drayXpk2bikqlEl9f33iPe48ePZLatWsrrSb1+eQv5onfiRMnpFGjRmJtbS3+/v5aY1VduHBBypcvL3PnzhUR/a5XzNgGDx4shQoVkpw5c0ru3LllwYIFGkn5a9euScWKFVNNK7XQ0FDx9PQUe3t7sbCwUPYh9bpU/3vp0iUxMzOT5cuXp1isCRVzO1y7dq20bdtWMmXKJAUKFNBqffz27Vvp0qWLtG3bVkT0f33FjK9fv36SM2dOcXNzEzs7OwkICNA6BgYEBEjz5s3l06dPel23mOvs999/F5VKpbR8iv28yNduu+7u7jJhwoRki/FHxYz96NGjMmHCBFGpVNKgQQMlwaT24cMHyZ8/v/z+++/JHWaixdyeBg4cKIULF5a8efNKjhw5ZPTo0Rrd/f/55x/x9PRUjh+poQXeiRMnxNPTU2xtbSVTpkxKciamiIgImTp1qlSsWFHu3bsnIvp9DIn5vW/fvl2mTJkiKpVKatSooYzpFDN+Dw8Pady4cbLHmVgxY+7fv78UKVJEHBwcxMPDQ7kpqXb+/HlxcXFRzj3+a5h4ojQp5sHt2rVrkitXLrG3txd/f3+NxEzMcSWOHj0qXl5e8uzZs2SP90dMnjxZzM3NJSgoSO7evStz5syRUqVKSdWqVbVO/tq1aydNmzbV6x8ktW3btomjo6NyZ+ro0aOiUqlkzZo1GuVCQ0OlbNmyMmTIkJQIM8Fi3x0uXLiweHh4aJwoqBMBMccPyps3rxw/fjzZ4/0RY8eOFUtLSzlw4ICcPn1ali5dKvb29lK+fHmNsWjCwsLEz89Pr8cdi3nXcMeOHeLo6Kh08VF3XY2ryX6zZs1kyZIlIqLfJ35qYWFhsmbNGlm5cqWYmppKgwYN5MGDB3GW7datm5QqVSqZI/xx6i4yHTt2lDp16kjGjBnF399f62731KlTxd7eXqs1pb4aMWKEZM2aVbZt2yYRERFSvnx5cXR01LirGh4eLp06ddL77ltRUVHKvjZ69GgxMjKSggULyp49e5REdezWdwMGDJBevXppPKfPevfuLXZ2djJp0iQZNWqUFChQQEqUKCFLly7VKHf69GmxtLTU+66EMb/zadOmia2trRw5ckREvrZOMDMz0+pyvGrVKilTpkyqGfT+0aNHcvLkSdm4caO4u7tL4cKFldaDMbvFi3z9PfDy8tL7m5VqvXv3luzZs8uIESMkICBALC0tpXLlynLjxg2NckeOHJFSpUopQxnou5EjR0qWLFnk0KFD8urVK2nXrp2oVCq5fPmy1o1aZ2dnvW8NGrv7tEqlkpw5cypd0mKeU4l8bdGVL18+6du3b/IH+4P69OkjOXLkkFGjRknLli3F1tZWypYtq7RuUtfx4MGDUq1aNb3dx2InMEeOHCmZM2eWw4cPy9OnTyUwMFBUKpWcP39eY1ucO3eu5M+fP1X0RElqTDxRmhZz9rPTp09Lzpw5pVGjRhpNv2MeDLy9vWXmzJnJHmdiqFss+Pn5yR9//KHx3N9//y158+YVX19fjYusnTt3ypgxY/TuLs/ixYu1TnpWrFghvr6+yv/Nzc1l1qxZIvK1i8zly5eVE8CdO3fKgAEDlMFZ9dmaNWvk9u3b8vbtW9mxY4eUL19evLy8tE7u3r17J2FhYVK3bl3Zt29fCkWbcOHh4dKgQQPlglDk60nDwYMHxcbGRqpVq6aRzDl06JB07txZ7wYZnzx5stL6TP3vzJkzpXLlyiLydVY0c3NzpUvMhw8fNBKDI0aMSDWDRk6fPl2aN2+u/H3q1CnJkCGDNGjQQGO8HXXrjODgYBkxYoTGILv66sCBA2JjY6PRwnPRokVSsGBBadasmUb3ktevX0u9evX0shthzAHdo6Ki5P3791K+fHlZtmyZiHy9W2xpaam0bIq5jz148EAKFCiQKlrg3blzR65duybnz58Xb29vKV68uGzatCnOMWZWrlwp2bNnTxWJwmvXromzs7PGjHznzp2T+vXrS5EiReTvv//WKN+5c2fZsGFDcoeZIDG7LsVsLameHXL9+vUa2+Lnz5+ViTNEREqXLp0qxpmcMmWKRnfOixcvipubmxQuXFijG+GiRYvk/fv3cuXKFRk2bJje/ZbF5fTp05IlSxYJCgpSlv3777+SJUsW8fHx0VjHt27dkl9//VWvW5Orz9s/f/4stWrVUravjRs3SqZMmZRtMWbCMzg4WOrUqaPVyktfPXjwQPbv3y+rV68Wb29vcXFxUW6Mq5Pz6nP6TZs2Sb169TT2O311/vx5sbGx0Rja5NKlS+Ls7CzlypXTmM3u/v374uzsLAcOHEiBSBNG/VulvrGqPrZv2bJFrKyslJZNMSctuHHjhlSrVi3em31pGRNPlGbdvHlTsmbNqnFRcfjwYcmZM6c0a9ZMuTDx8vJSfqSuXLmilxchcWnRooXUq1dP6wS9d+/eYmxsLDVr1lROHMLDw+XUqVMpEWa8goKCxMDAQHr06KGM6SEiMmnSJClfvrwcOXJELCwsNBKBy5Ytk65duyoXwMHBwcrYNPrswYMHolKp5NChQ8qyjRs3KuNUqZNPLVq0kJUrV4rI1wvoPXv2pEi8ieXp6SnVq1fXWBYdHS29evUSlUolVatWVZa/f/9e70789uzZIw4ODtK0aVON/Wn58uXSqlUrWbt2rZiZmWmMw7Jlyxbp2bOnciIYFRWVasYy+f3336V8+fIi8r8T19OnT0vGjBmlbt26smXLFqlRo4bky5dPmVUn9rgZ+urQoUNia2ur1bVu3rx5YmBgoNXyacqUKXL+/PlkjvLbateuLYGBgRoDAD9//lxy584tL168kKCgIDEzM1N+t0JDQ2XKlClK0jAsLEzmz5+v99NRr1mzRvLly6dcRKmTa8WLF1fGuBP5OhGD2owZM1JFS4zHjx+Lra2tVsLl4sWLYmNjIwUKFFCSiCJfu+vq43hjnTp1ktKlS8uxY8eUZZ8/f5by5cvLvn375MSJExrHxvDwcJkyZYrs3btXObYcOHAgzrGS9M3o0aPF3d1dY9m///4rhQoVEldXV9mzZ49UrFhRY0a71FAvEZGTJ09K9uzZ5datWyLyv4TMqVOnxNjYWJo0aaLxu7xx40alNZu+8Pf3F29vb+XvqKgoef36tdjY2MihQ4dk//79GsfFL1++yIABA5Tf5cjISBk3bpzWb4M+mjBhgjRs2FD5+/jx4+Lp6SkuLi4SHBysLF+xYoW8evVKLly4IN27d9f71lwiIseOHdOYHEh9nDhx4oSYmpqKn5+f0vJJRGTfvn16dxMlMDBQihYtqvwdFRUlISEh4uTkJLt375bdu3drbIvh4eEyZswYjQTapEmTUtUQKEmFiSdKM2K35gkLCxM7Ozsl2xxzbKB8+fJJ8eLFxc3NTfLkyaOc+Opjc/D4WimNHj1abGxstE4O5s6dK76+vuLj4yM9evTQ6xkhFi1aJA4ODtK9e3e5ffu2iHydxShXrlyiUqlk4cKFStlPnz5JtWrVpFWrVnrfzSJ2fK9evZKcOXPK7t27NZZv2rRJKlasKHZ2dlKuXDnJkSOHXq+v+LbFJUuWiJubm8YYaiJfL/b9/f3F2dlZAgICkiPEHxIaGirz5s2TYsWKSZMmTZTkU1BQkGTMmFFUKpXS6k5d3sfHR9q3b6/322Jc62zt2rVSvHhxCQsLk+joaOW4d/bsWcmdO7cULVpUSpcurZfHw5jimqr+0KFDYm1trbQWVNchIiJC8uTJIwULFpROnTppnNjqm1mzZolKpZIBAwZoJJ98fHykfPnyYmZmpnFsfPjwoZQtW1ajFU1qaBW0ePFisbW11TjmhYSESIUKFeTXX3+VAQMGSNWqVcXS0lJZj/o4vXZc+9ijR4/E3d1d+vfvL1++fNHYRmvUqCGlS5cWHx8fjZsR+ujcuXPi6uoqNWvW1Jg5t0uXLmJjYyMmJiYaCbTXr19L+fLlZdKkScoyfWyRHN9x0cnJSeu5W7duyW+//Sb58+eXChUqpJrjYkwPHjwQExMTjTHtoqOj5dWrV+Lq6irp06eX2rVra7X61Sdbt24VGxsbadCggcbywMBA8fPzE1NTU1mwYIGy/MmTJ+Lj4yNLlixR1qm+/16rTZs2TUqUKKER74kTJ8TLy0tJbnh7e0vp0qWVuuljQj6u7/vt27eSJUsWGTNmjMbyFy9eSMGCBSVDhgzKjTER0bsWhZGRkbJ27VrJly+f+Pj4KMvDw8OlTZs20qBBA7GwsNAYw+nBgwdSvXp1WbZsmd71PEluTDxRmhMWFqY0B69UqZKMGzdORDRn1zp37pxMnjxZRowYofzA6uPJRMwD1ObNm2XDhg2yY8cOZZmvr69kz55ddu7cKY8ePZLQ0FCpVauWzJ8/X0aNGiVWVlZas2Dog5j1Wrhwodjb22sknxYsWCC5c+eW5s2by7Vr12Tnzp3i6+srbm5uyvpKDQfvmP3Sa9asKf379xcRzdhPnjwpo0aNkt69e+v1SV/MmIOCgmTdunVKS7UHDx5I7dq1xdfXVxm/5NWrV1KzZk0ZM2aMTJ06VfLlyyd3795Nkdi/RX1i9OnTJ5k3b554eHhIo0aNlOPB3LlzRaVSyahRo2Tfvn1y7NgxqVSpkhQuXFhrYHh9tnDhQtm1a5ecP39e1q5dKzY2NlrdXEW+tqy5ceOGsr71cVsU0dweP3z4oLEO6tWrJ3Z2dhr1e/bsmfj7+8vw4cPFyspKo8uJPlq6dKmoVCrp37+/cgz/66+/xNnZWeNk9+PHj1K1alWN8dT0UVz7yKNHj8TFxUVpTadOUHz48EGaN28uvr6+UqNGDb38bVaLPdvgy5cvlWUzZ84UAwMDmTVrloSGhorI16R1w4YNZe7cueLi4qJMVKCP1NvT5cuXJX/+/FKjRg0lUXbr1i2pVKmS5M6dW96+fStRUVESHBwsvr6+UrJkSb3eFmNaunSpTJ8+Xc6fPy+7d++WggULxtvF7OrVq6nquBgcHKwxmUT//v3FwcFB4wbRx48fpWPHjhIUFCQmJiYyffr0ZI85oaKjo2Xfvn2SJUsWqVevnrJ8zpw5kjVrVvHz81MS9a9fv5aqVatKuXLltMZE0jfxJUGtra21krbnzp2TGjVqiJOTk3h7e2uNOxb7/ykpZr1ev36t0dWsf//+UrRoUY1E4cePH6Vly5ayf/9+yZw5s1ZiSp98+fJFtm3bJi4uLspwDCJfb8KqZ3ZW3/x5+fKl1rb4X8bEE6Ups2fPFisrKylVqpR06dJFChQooAzi/K3xSfTxYBDzx6N79+6SOXNmcXJyEnt7e+nYsaPyXK1atcTe3l4cHBzExcVFcufOLSJfB+XOmzev3t0hVtcr5nc+f/58yZ49u3Tr1k2ePHkiYWFhsnTpUnFxcZFMmTJJkSJFpFatWsqPrD6ur9hGjRolBQsWlBIlSkj79u0lf/78EhAQIK9evfrmHWB9r1vv3r3F3NxcHB0dJV26dDJt2jQREbl+/bo0adJEfvnlF7G3t5e8efOKq6uriHztlubs7Ky326L6BCksLEzmzJkjRYsWlYYNGyrb2/jx4yVnzpySKVMmKVGihFSpUiVVbYvnzp2TYsWKiaOjo2TMmFFKly4tKpVKfHx8ZPr06bJmzRp59eqVRpdXkdSR3B09erSUKVNGateuLZMnTxaRrwmMSpUqSaZMmWTChAkyb9488fb2Vrpp5M+fX7p27ZqCUccv5nF/yZIlolKp5M8//5SwsDD5+PGj9OvXT/LmzSu//vqrNGrUSEqVKiWFChVKNdvj6NGjZfDgwTJ37lzZtGmTZMqUSTZv3qxVLiIiQt6/f698H/p6oa82cOBAcXZ2Fjc3N6lbt65yo2vEiBFiaGgoDRs2lN9//13Kli2rdOdq2rSp+Pr66vV+pt6e/v33X8mXL59Ur15dGQR9y5YtUrJkSbG0tJSiRYuKh4eH/Prrr6liW4yOjpYrV65ImTJlxMHBQfLkySM2NjaiUqmkZs2a0rVrV9m2bZtcuXJFKxGlz+tLbdiwYVK6dGn59ddfZd68efLmzRt58eKFdOjQQTJlyiQ9e/aUqVOnSoUKFcTDw0OioqLE09NT49xSH0VHR8vevXslS5YsGhMoDB48WPLkySNFixZVkp9FihRJFdui2vLly2XSpEly+fJl2bRpk5QuXTrOm0MikipuDqkNHTpUPD09pUCBAvLXX3/Ju3fv5PHjx9KuXTvJkyePtGnTRmbOnCmenp5SsmRJ+fjxo5QrV066dOmS0qF/05cvX2Tr1q3i4uKi0QV00qRJYmlpKb/99pt4eXlJmTJlxN3dPVVti7rExBOlajG7WkRFRcmpU6dk4cKFMnnyZGnUqJGUKVNGVCqV2NnZScGCBcXb21tq1qyp1328Y9+tePLkiZQsWVIuXbok169fl8WLF4upqam0bt1aKbNlyxZZunSpLF68WDmodejQQYoXL65Xgw3GPGGLefdD5Otdq+zZs0vXrl01usFcuHBBnj9/rvcXILHX26FDh2Tjxo0SGBgof/zxh9jb24tKpZIKFSpIjhw5pHbt2tK8eXO9HNcjppg/kidOnJBixYrJ0aNH5d27dzJs2DAxNzeXUaNGSVRUlLx9+1YuXrwoY8aMkRUrVijrqlOnTlKhQgV59+5dSlVDS+yLB/VJgXp8nCJFimi0fLp7965cu3ZNHjx4oPfbYnwXRq9fv5b79+/Ltm3bxM7OTry9vaVgwYJiZ2cnNjY24ufnl8yRJl7Muk2dOlUyZ84sQ4YMkdq1a0v+/PmlU6dOyvOdOnUSDw8PcXV1lapVqyoJ3+LFi2t0ndQH8a2zhQsXikqlkr59+0pERIR8/PhR9u3bJwEBARIYGChjx47V65aSMT179kzatWsnFStWFEdHR+X32crKStq0aSM9evSQffv2aYzvJKI/d/Bjirm+1q5dK1mzZpWVK1fKqFGjpEiRIuLq6qokn1atWiWtWrWSSpUqSevWrZXfvmrVqmlNta0P4tsWL1y4IPny5ZOqVasqswO/efNG5syZI1OnTpW///5b+b3Qx20xvnqFh4fL8+fPZfv27ZIlSxapXr260qo1ffr00rhxY73cBmOKWbd58+ZJ5syZZdasWVKrVi0pWrSodOrUSV6/fi0hISEyc+ZMyZMnj5QqVUqjRaGXl5cMGzZMRPRnn4trnUVERMjevXslc+bMUrt2bWX55s2bZezYsdK1a1eZM2dOqjkuRkdHy927d+XXX3+V3LlzS758+SRTpkxKErRz586yfft2OXnypNa1iz4mQWPGNHv2bMmSJYtMmTJFGjZsKDly5JDevXvL69evJTg4WJndrVSpUlK9enXlmFmpUiWlNag+b4thYWGydetWyZMnj1SsWFFZvnXrVpk4caJ07dpVFixYkGq2xeTAxBOlWjEPAnG1IImMjJTNmzeLl5eX7N27V7Zv3y5dunSRDh066O3OHztJNHHiRKlevbq0atVKOTn49OmTMgV6zOST2oULF6Rt27aSOXNmuXjxYrLEnRAxfzwmTpwoNWvWlEaNGiknOiL/Sz517949zkE79fFHVkQ7rri2x40bN4qDg4OsX79epk+fLn/++af4+fnp7d2P2AOAT5o0SXr27KnVUmTkyJFibm4uY8aMkVevXmk8d/HiRenWrZtYWFjo1bYY0+TJk6VJkyZSo0YNWbJkiXz58kUiIiJkwYIFWsmnmFLDtnjy5EnZvXu3VreyyMhIKVmypEyYMEFEvjZxP3nypN5ui2qxZ0ccM2aM0vX4zZs3Mn36dMmZM6fGXfvnz59rzBA3YMAAsbe3V7r16oOY6+zcuXNy5MgRefTokbLdzZ8/X0k+xaxLTPq47r63jzx48EBq1qwplStXlmbNmkm5cuUkT548UqZMGb252IhLzNhWr14tCxculCVLlijPnTp1SgoWLCj58+dXLqRi/ia8efNG/vzzT7G2tparV68mb/DfEXOdbdu2TRYsWKB05Rf5OiOVOvkUc1bPmPR9Wzx+/LisXbtWTp06pTWrVOPGjaVt27Yi8vWi8sqVK3pZn/j8888/0qlTJ9m4caOybOzYsVK8eHEJDAxUJsP48OGDxnfSu3dvjcHH9UHs37I9e/bIo0ePlMTtnj17JFOmTBrJp9j0dd3Fl1ATEbl3754cOHBALC0tpWTJklK/fn1xdXWVdOnSSfPmzfX62BjTpUuXpFOnTrJlyxZl2cSJEyVv3rzSq1cv5QZzZGSkxjlWr169xM7OTm+3xaNHj8r+/fs1jt3bt2+X3LlzaySfYtPXbTG5MfFEqVLsu941a9YUb29vadOmjcYB7N9//xUjI6M4Z5vSt4NAq1atpFatWiLy9eQ1NDRURowYIdbW1hqzqIj8L/lkbm4u9evX11i+fft2qVSpkl5d6Mf8oRw9erSYmZlJjx49pHbt2kq3EbV58+bJL7/8Iq1bt9brAYDVYm6LkyZNkubNm4ubm5vMmjVLaaIfFRUlR48elZw5c8Y56K++JTHq1asnvXv31ljm7+8vKpVKvLy8tGZOGTVqlGTOnFkGDBig0aV11qxZUrVqVb3aFmN+1/379xcrKysJCAiQxo0bi6GhobRu3Vq56J83b54UL15cfHx89O548T19+vQRV1dXcXZ2lhIlSkiZMmU0jo29e/cWf39/rdfpYz2bN2+usc0dOnRI7O3tJWvWrEq3H5H/JZ9y5cql1WXk+vXr0rp1a8mWLZveThPeo0cPcXBwEFNTUyXpqR4bSJ18GjBggF6O2xdb7PEJZ86cKRMnTtSKvV27dhpdZl68eBHnoPH64LffftNItty8eVNpyTp//nxluTr5VKhQIXFzc9MYX/Lp06fSqVMncXR01LvZFGN378+SJYs4OTmJi4uLODs7K62czp8/L/nz55datWopg/inFr1795ZcuXJJ/vz5pXTp0lKmTBmN40GXLl2kSpUqWq/Tx+Ni586dNWbF2rVrl+TJk0fs7Oxk586dGmXHjRsnJUqUkMDAQLl3756y/Ny5c9KtWzfJnj273h4Xe/fuLZkyZRJbW1uxsLCQtm3bKrOc7dmzR7JkyaI14Lg+i3lsPHLkiKxfv16OHz+uNQtp27ZtpV27diIi8u7dO7l//75ebociX9dRzGOjelY3GxsbjSSoyNfzZBcXF+nTp4/GDc6zZ89KYGCgODg46O222KtXL7GyshIHBwcxNDSU9u3bK7Pkbt++XVxcXDTGYCRtTDxRqta3b1/Jli2bTJgwQf7++28xNDSUGjVqyMePHyUyMlLevn0ruXPnlr1796Z0qN917949jVZNIl9PUidPnizp0qXTmFJa5GtXtQULFkiFChW0Ehf6NguE2unTp6Vx48aya9cuEfn6A3zs2DHJnz+/eHp6KuWmTZsmtWrV0rsLj29Rb4tjx46VSZMmiZWVlTRt2lRJNIWHh4ujo6NWNxJ9dP78eeViKWbyr0+fPqJSqWTRokUSFham8Zp+/fpJ5cqVtdaZPnWvi+nOnTvSu3dvjVkhd+7cKTY2NhIYGCgiX/ejSZMmSatWrfQuOfgtkydPlixZsigXJWPGjBGVSqVxkTht2jTJlSuXXg/cLPK1BWezZs004rx586b069dPrKyspG/fvhrl3759KzNnzpSMGTPKxIkTleWvX7+W7du3621Lpw0bNkiePHlk3759cunSJZk2bZr8+uuv4uXlpSSf1GM+xZwtR9/16tVLnJ2dxcvLSypVqiRGRkZy5MgR5TixadMm8fDwkE+fPmkcO/RtfwsJCZHhw4drdBEPDQ2VjRs3SsGCBbVuDkVHR8uZM2ckW7Zs0qxZM43lt27dUloQ6YuY3/2RI0ekZMmS8s8//8j79+/ln3/+kfr164uZmZmSLLt06ZJkzpxZ6waFPps9e7Zky5ZNmZ1vwIABYmJiItu2bVPKqC8e9X1WyNOnT0tgYKBW6/2ePXuKtbW1tGvXTmtc0wkTJoizs7PGcTEkJES2b9+uVzOixdwW9+3bJzlz5pSgoCAJDg6WRYsWiaenp9SpU0fpdrZv3z4lKZ+a9OnTRxwdHaVo0aJKK8KYs1x26tRJypUrJyKax0N9Sz79+++/0rJlS61tccCAAZIhQwbp1q2bBAcHazw3ZcoUsbKykpkzZyrLIiMjZcuWLXq7LZ46dUp++eUXOXTokDx58kTWr18vbm5u0rhxY2XMrW3btomVlZXejiGpD5h4olTr8uXL4urqKvv37xcRkR07doiZmZnMmTNHo5yrq6sMGTIkJUL8IQsWLBAbGxul21JwcLCMHz9eLC0ttWbAiXkxpm8n6rGtWrVKihUrJrlz55YrV64oyyMjI2XXrl2SL18+jRPA2AM/67OTJ09Knjx5lPGaTp8+LQYGBso001FRURIZGSnZsmXT+4vGmD+006dPlypVqmjcyerQoYOYmJjIX3/9pdWlMGZLBX1OGm7YsEFUKpVkz55dTp06JSL/2842bdokBgYGSkIq5lTo+rotxvyuIyIipFWrVjJ79mwR+VofCwsLmTdvnoiI0lVrzZo1UqNGDb1eTyKa29LcuXOV4+KDBw+kf//+kjt3bhk5cqTGa16/fi3r16/XuxP0+GzevFm6du0qf/75p7IsPDxctm3bJkWLFpX+/fsr296OHTv0tqt4bH/99Zdky5ZNaXG8fv16UalUsmHDBqXMyZMnxcjISC9nvIzPqFGjlAHRP336JFu3bhVnZ2eN2Y1Evm67165dSzXbocjX3+nGjRtL/fr1NY4Njx49kpo1a0rFihWVhMbdu3dTRd3Uv7+tWrWSwYMHi8jXfc7c3Fz5PQ4NDZXXr1/Ljh07pFy5cnp/XBT533F/+fLlGgP09+jRQ4oUKSIjRozQuvGzYsUKvZ/lTW3GjBkyfPhwreTmxo0bpXDhwspxPyIiQk6fPp0qtkW1OXPmiJ2dnZIEHTRokGTMmFF2796tlNm+fbsULFhQq4W5PlJvS3///bfG8b13797yyy+/yKRJk7SSuatXr1bWmb6eW6mNHz9eBg4cKD169NBYvnv3bnFwcFCuzcLCwuTYsWOpaltMbkw8UaqhPjCpD3BBQUHKDG6bN2/WSDq9f/9eueifPXt2qjlRF/maUHN3dxdXV1flIuvFixcyceJEyZQpk8aYSKnJ1atXxcfHR9KlS6c1TeqLFy8ke/bsWklDfT0xiv2jcuTIESlRooSIfP0xNTMzUwYv/vDhg+zfv1+ZqS81bYsHDhwQBwcHadKkiUaXpg4dOkiGDBlk+fLlWi2f9HWdxXT27Flp0aKFpE+fXmmBpk4whYaGSp48eWThwoUar9HXesV1wlauXDmZPXu27Ny5U2NbjIyMlIkTJ8ratWuVCRniew998/DhQ3FxcZH8+fPLmzdvROTrhW///v3FxcVFRo0aFefr9P0E8N27d+Lk5CQqlUqj27RamzZtxNvbW6se+ngcib0dDRs2THr27CkiXwfgjnmh//79ewkJCZHbt29L48aN9X49qX369EmaNm0qKpVKabn76dMn2bJli+TNm1d8fX3jfF1qqZ+/v79YWlqKi4uL0upVfeybP3++ODs7K+MEqel73dT7ir+/vyxfvlx27dolZmZmSnI+IiJCFi5cqJwz6vuNhpju3bsn5cqVEy8vL42kRefOncXDwyPO5JOI/q+zyMhI8fb2FpVKJVWqVNHorirytbVQjhw5tM4/9PG4GJN6m2rTpo3SWnfDhg1iYWGhnP+GhYXJ27dvZc+ePVKyZEm9PfeIKTo6Wh4/fiwFCxaUqlWrKmMvinzttuvo6CiTJk3SGgdURP+3xdDQUKlbt66oVCqpWrWqiHxdj+r1Mnr0aMmWLZtWC0N9r1dKYeKJUoWYO7S6VcmjR4/E09NTmVkrZkuSU6dOiY+Pj8bgdPp4EIjrxCY6Olpu3LghHh4e4uLiopF8mjRpkqhUKq2LYn0T3wnbnTt3pGrVqlKqVCn566+/lOWhoaFSoEABvW8NJKI5G9+ZM2fk06dPEhQUJL/88ossW7ZMLC0tNZoP79q1S+rXr68xroI+nhzFt84OHz4szs7O0rBhQ43kU8eOHTUuvvRVfPW6ePGi+Pn5iZmZmTJ2icj/EgHqwYL12ZkzZ5S7iL1795alS5eKiMiff/4pXl5eYmFhoTF72/Pnz6Vq1aoyadIkZZm+ntTGjisqKkoOHDggZcqUETc3N3n9+rWIfE0+DRgwQAoUKCD9+vVLiVATJa7v+9GjR1KmTBnJmTOnbN68WeO3as6cOVKkSBEl2aav4vp9/eOPPyQgIEC2bdsm5ubmGtvijBkzlK4x6u8ktfxGv3r1Sjp06CBGRkbKWDrq5FP+/PnFw8MjucP8IXHVLSoqSvr06SN2dnby559/KvuZiMixY8fE2dlZ7wZEj+3AgQPK/4cOHSrjx48XEZGuXbtKlixZxNzcXBYsWKCUefnypVSsWFHGjh2rLEstx0WRr13Ea9WqJd7e3hq/x126dJHixYtL7969452UQF/EtS2GhoZKy5YtJWPGjLJnzx6Nui9fvlyKFSumt13546MeBqNJkyaydu1aOXz4sMZN84iICJk7d66sXr1aI7mhj0nQuLbFw4cPi5eXl9SoUUO2b9+uLO/Ro4c4OzvLsGHD9H6dxfVdP3r0SDp27CjGxsYaCV6Rr13gixYtqvf7mL5g4on03pYtW6R169YSHBwsnTp1EkNDQ3n58qVyEWVkZCR9+vRRyn/69EmqVq0qderU0cuDtVrM2DZu3CizZ8/WGIvq5s2bWsmnZ8+eyapVq/QycaEW88do6dKlMmzYMFm5cqVyl/TatWvi6+srLi4u0rZtW5k8ebLUqlVL8ubNq9f1EhHZu3evMnBgly5dNE58ateuLSqVSqPlxadPn6R69epSt25dvd4WY66zjRs3yty5cyUoKEiZZfHQoUNK8inmYKYTJkzQ63UW+0R15syZGgnPq1evSq1atSRDhgwyfPhwmTp1qlSvXl0KFCig1/WKioqSR48eiUqlkq5du0r79u3F3NxcGfPi4sWL4uDgIEWKFJHLly9LVFSUPH78WKpUqSIlS5bUywv8mGLuKyEhIUrSJSoqSmldGDv51LlzZ72f9jxmvR48eCBhYWHKHfv79+9LkSJFxNPTU1asWCGhoaHy/Plz+e233/R+vLsDBw4o44e1bt1aGdh9/fr1UqxYMTExMZFp06Yp5d+/fy/VqlVTWkPpq5jr6+7du3Lt2jXl75CQEGnXrp1W8mnNmjXSqFEjvT7ei2jW7fTp03Lz5k15+PCh8lzHjh2lSJEi8vvvv8uNGzfkwoULUrlyZSldurRe1y04OFhsbGykQoUK0rlzZ8mYMaNcunRJRL7Wy9fXV+zs7OThw4cSHBwsT548EV9fXylRooReH/NFNNfZ8+fPNW7I7ty5U6pXr66VfGrZsqW0atVKr48fsY+LMQfZDg8Pl5o1a4qNjY2sX79e7t69Ky9fvpQKFSpIpUqV9LpeIl/PGdXb1dChQ2X69Oki8nViE2NjYzExMZGVK1cq5d+8eSMVKlTQOI/UxzrGXGePHz+WDx8+KK3SDh06JOXKldNKPrVu3Vrq1q2rl/VRi12vmONNvXz5Upo2bSqmpqayYcMGuXv3rrx69UoqVaqUKrZFfcHEE+m9devWibW1tbi7u0uWLFk0xgc6deqU5M2bV3x8fGTo0KEyd+5cKV++vBQsWFAZ/0gfT5JiHqD69u0rpqam4u7uLiqVSrp06aJM8Xvz5k0pVqyYuLq6as0GpO8nSerpot3d3aVAgQJSrVo1uXPnjoh8nWGqWrVqYmBgIFWrVlXuSIro511vka/b0dKlS6VEiRKSL18+yZQpk1Ifka9dP728vMTFxUXWrFkjM2fOFB8fHylQoECq2RZ79Ogh2bJlEwcHB3F1dZXWrVsr292hQ4ckV65c0rhxYzl8+LDGe+j7tti/f38xMzOTkiVLSrp06cTf31+54L969arUq1dPVCqV+Pn5yd9//62MXaWv26La4cOHxdjYWDJkyKCMdafexk6ePCl2dnbi7u4uTk5OUrp0afn111+VbVHf6yYiMmTIEClTpozkz59fI3GhTj4VKlRISUo9ffpUb2dDi23AgAHi6uoq+fPnl2HDhiktc+/duyceHh5iamoqhQoVktq1a0uFChWUVpb6Vq/o6Gj58OGDuLq6Svny5aVBgwZiZWWlzPITEhIiDRs2lFy5csmCBQvkxYsXcvHiRalSpYoULVpU748ban369JFcuXKJhYWF1K5dWy5fviwiX7tRt2vXToyNjZWL/ZhdgvTxeB9b7969xdbWVrJmzSo1atRQpj6PioqSzp07i7m5uWTOnFn8/PykefPmyrFRn+t2/fp1MTU1lYwZMyrbonofunz5shQpUkRsbW3F0dFRSpQoIcWLF09Vx8XBg/+Pve+OiirZvu5rAANIFgVEQEVBAQkiQQmSg2BOI6YRRUVFQREjoIiimEkGVFRUUMCIOYtiQMecc8ZEzr2/P/rrmnu7G8O839Pqeey1Zo3cW93rVNepU1W7TpgHAwMDWFpaYuzYseS5kHxycXHheGWIpqmgFTNmzEDHjh2hpKSEmTNnkopnVVVV6NWrFxiGgaamJoYPH86p0kqrLr59+5Z4QE6cOBGysrKkwm9RURH69+8PZWVlvHr1Cl++fMGrV6/g7u4OS0tLqbGNQl20sLDAhAkTOJeV3bt3h4+PDyfsTlrW6BkzZqB9+/ZQU1PDhAkT8O7dOwCC/JFDhw5FvXr1oK6ujnHjxsHKyop6XaQJdcRTHaQCQ4YMQb169dCvXz9yKyfE+fPnERAQAD09Pbi5uWHUqFHEaNNuvG/evAk7Ozvk5uaipqYGe/fuRbNmzTB27FjCtD98+BDa2toYNGjQb5b222BvbkpLSzFo0CBcu3YNfD4fO3fuhJOTE+zs7EhVqYcPH8LDwwN9+vTBjh07yPfQviANHDgQDMPAwcFBTNazZ89i6NCh0NDQgJ2dHUaMGEEWJNp1UXggzMvLw9evX7Fy5UrY2tqif//+HPKpadOmYhUWaQNbFwsLC9GzZ09cunQJX79+xenTp6GkpIR+/fqRamE3btyAn58f1NXVSdidaOJ0msDn81FVVUXGg2EYTJ06FW/evCHvAcEcy8zMxOLFi7Fv3z5yqKJVF9mbthUrVqBly5aIjo7G5MmTUb9+fUyaNImM2blz52BjYwN1dXVO8lXa7ceuXbvQqlUrpKenIyAgAN26dcPAgQNJ+NKLFy9gY2ODdu3aYfPmzWTMRHOc0ISioiJoamqifv36JIm9EJ8/f0bv3r1hZGQEWVlZWFpawt7enuqDPlsPU1NToaenh507dyIrKws6Ojro3r07CTsuKipCQEAAGIbheIPSCvb8uHDhAgwNDZGTk4PNmzdj6NCh6NixI9LT0wEIfoeQkBB07NiREyLDDjenEdeuXYOSkhLU1NTg7u4uUce2b9+OjRs34sCBA1JlFzdt2gRVVVUkJSURQtTBwYG8z87Oho+PDzp37szRR9oPxLt374aenh62b9+O5cuXQ01NDUOHDsXVq1cBCDyfRo4ciXr16nEiA2gdM0Dwm1+6dAmKiopo3LgxKWQitH0XLlyAg4MD5OTk0K5dO5ibm6Nr165SZRtVVVWxefNmTJ48GTY2NrC3tyeeeKdPn4aDgwNsbW05BWpo18Vdu3ZBV1cXW7ZsQVJSEhQVFeHp6UkumvPz8xEYGAiGYYi3K0C3LtKEOuKpDlRCuDkSTuQlS5Zg+fLl0NbWxpgxY8gmnR3/XFxczKnyRrsRWLhwIYYMGYJhw4ZxZN2zZw8UFBQQEBBAPJ9evnxJ5SIkBHshuX//Pu7fv48ePXpw3FSzsrLQo0cP2NvbEwN+584duLu7w8XFheSnoQ3s/CNlZWVISkrCokWLiCuxpHj1z58/c8aLdl3cvn073NzcMGjQICJrTU0NkpKSYGtriwEDBhDy6dq1a1Kji0+fPkVeXh7GjRvHKed74cIFKCkpoX///oTIuHnzJvr37w9NTU3OJokm1LZhO3LkCBiGQWBgoFjiX1HQPHZCXL9+nZBlQmRlZaFBgwaYOHEi8VY7fvw4/P39qe6T6Jht27YNMTEx5G9hefD+/fuTUK5nz57BxMQETk5OOHXqFNX9q6iowMOHD2FhYYEOHTrAzc0NR44c4bQpLS3F48ePkZWVRUI/Afrt4oEDB7Bo0SJO7sF3797BwMAA3bp1Iwf7wsJCLF68mPr+iOrimTNnEBgYSP6+evUqRo4cCQMDAw75NHbsWFhYWGDx4sVU5hqrLQ/XX3/9BS0tLTg7O4u1EZ1TNM8xIfbt24fk5GTs3LkTgIDAOHbsGLS0tDjkU2ZmJqZNm0b1AV9UtiNHjnA830+cOAFdXV388ccfyMvLAyDor5eXF9TV1UmuV1oh3Dfm5eVBV1cX7dq1g42NDdlvsLFz506kpKRIBQkqRFZWFhYtWsRJyJ+VlYWuXbvCzs6OkE9HjhzBuHHjpEoXT5w4wfGwvn//PpSUlODh4cEhn4YMGYKmTZuSdYD2Sy9aUEc81YE6sI2AaAWEHTt2QEtLC2PGjCFuuADENrrSYADWrFkDhmHQvn17vHz5kvNu7969UFZWxsCBA4mLJ0D/5mjGjBlQUVFBhw4doKamxglFAwSkmouLCwwNDUkc/8OHD2FtbQ1fX1/qysaydbGiokLs5tHKygo9e/bkyH3q1CnibgzQr4vV1dUICQmBrq4uOnbsyHnH5/Oxdu1a2NnZwdnZmXPooF0Xp02bBj09PWhpaUFVVZWULRbi4sWLUFVVhZOTE7nBv337Ntzc3KCvr4/y8nKqxo6te8ePH8fOnTvx5MkTQsJkZmaSUN3Xr18DAPr160cOkNKCy5cvg2EYNGrUiBywhOOQlZWFhg0bYvLkyWIbeBr1ka0/a9euxbx58zBkyBCsWLGC0y45ORkODg4YNGgQyUfz/PlzWFpawsLCAqdPn/6lcn8PtR0i3r9/j86dO8PJyQlHjx795vyh+SDC5/Px6dMnMAwDhmHEPDzfvXsHQ0ND2Nvbi40N7QdGAFi0aBH69OkDHx8fDBs2jPMuLy8PI0eORKdOnUg+vJqaGgQFBaFt27ZYvnw5tXbx8OHDSE1N5aRjuHDhArS0tODm5ka8BkePHk0KgNDUl2/h1q1baNasGRo0aEDsIiCwe8ePH0erVq3Qo0cPsc/RbhcTEhIQGBgIe3t7zJs3j9NOSD75+fmRw31FRQX69OmDBg0acIqC0AJJdu3Dhw84d+4cTExM0LVrV7FKfOwLc4DOMWMjLy8PBgYGkJeXR1paGnleXV2NvXv3wtraGg4ODpzCBACdNl9UF4OCgmBmZiZm8x88eABlZWV4e3vj/v37AATjOnz4cDAMQ7zZ6vB91BFPdaAWCxcuhI2NDdzd3bFy5Uqyadi5cye0tbUxcuRIZGVlwcvLCxoaGuDz+dRuImozuFu2bAHDMJg5c6bYTWJaWhpcXV2pNNZCsGU7ceIEWrVqhf3792P58uWwsLCArq4uOQQLsWPHDkyaNImzuD5+/FgshJImREVFwcXFBU5OTti+fTsAwQEjJSUFNjY2cHFxwa1bt+Dq6go3Nzdq9RCQrIulpaWIjo6Grq4uAgMDSeUVQLAwL1u2DAEBAVKji7t27YK+vj42btyIjRs3QlVVVazKJSBwBXd3d+ccFu/evctJbkoDRPNwtWzZEgoKCjAxMcHChQsJ0ZmVlQVZWVm4urrCzMwM+vr6YptaacD69evRsGFDhIaGkrER/gZ79+4FwzBYvnz5b5Tw+2Dr44wZM6CoqAhLS0soKytDW1sbDx484LTftGkTDA0NMXv2bNLnp0+fwt7enni+0gC2LqampiIiIgK7du0i3q1PnjyBqakpXF1dcfDgQVRVVaF79+7UVxyUZLMfPXoETU1NWFlZcS66AAHJpqysjHHjxv0qEf8x2LoYFRUFFRUVDBs2DNbW1mAYRoycvnbtGnr16oUhQ4ZwPGCnT5+OJ0+e/FLZfxTTp0+HnJwc9PT0wDAMlixZQi4uL1y4AG1tbejp6cHKygpt2rSRCoKQjYKCAqSkpEBbWxu9e/fmvKuursaJEyfQoEED4sFG6x6ELVdkZCQaNWqEPn36QFZWFkZGRmKXyCdPnkTjxo0RHh5OPlteXo4hQ4YQAoAWsOfZwYMHkZaWRkJyhQShiYkJbGxsCPn0559/kgqLtI6ZKEpKSpCQkIC2bdvCycmJs8eorq7Gvn370KZNG4wfPx4Avf1iyxUVFQUZGRn069eP6OKpU6c47R88eACGYRAcHEyevXv3DmPGjOEUnqjDt1FHPNWBGogyz0pKSli6dCk8PT1hZWWFMWPGEM+E3bt3w8zMDEZGRrCzsyOGj0YDx16M7t27hytXruDt27eEeElISCC3qrW5sdN24BeVZ82aNVi1ahViY2PJM2H8ur6+vhj5JER1dTX1Y7Zo0SKoqalh2rRpJL/TokWLAAjIp7S0NFhZWaFly5bo1q0b1Qd9dr/u3LmDR48ekQNweXk5IiMj0bVrVwQFBXFu5dikLm26yPYuAwQbvpCQEImu0p6enmLkkxC0HkTY8+Ps2bOwtrbGhQsX8OLFC0ycOBFdu3ZFWFgY+R2OHTuGoKAgTJs2jfpcd9/SpVWrVpEDpGhC0rNnz1LbJ4A7Zi9fvsSUKVNw5coVAALizMnJCd27dxfTRUmhFjT1k92v6dOnQ1VVFcbGxmjXrh369OlDEuc+efIEVlZW6NSpE9q3bw8jIyOqc1Sx9bCsrAw1NTXkd7979y4UFBTg7e1N8hMKIRpSTTvu3LmDpUuXkkIEjx8/xvjx49GsWTPs2rWL0/bBgwfkd6FxTWPr4qVLl9C1a1ecP38eRUVFiI2NhZycHMLDw5Gfnw9AUHxg2rRpiIiIIGNL69iJ2kXh36Wlpdi2bRvJf8RGdXU1rl69Sm2fAO7vnZubi/Hjx5NCJefPn4eNjQ369OlDKmQKwe4XjftFUYSGhkJOTg76+vpgGAZRUVGorKwEn8/H8ePH0blzZ7Ro0QLdu3eHtrY2VTZeFKK6KJS1rKwMGzZsgLGxMQYPHixGPp07d45qXWT368qVKxg+fDjOnj0LQBCFYWBggJ49e5JnQrx8+VJsvGjbE9OOOuKpDtTh1KlTmDFjBvbs2QNAcCCOiYmBpaUl/P39Cfn05MkTzuaIRuMtWr3OwMAAjRs3hrm5OYYOHUr6IiSfwsPDxcILaYOtrS0SEhLI3wUFBbCysgLDMJg0aRKn7YULF+Do6AgDAwOqPZpqw71797B8+XKSzLKqqgpr1qxB/fr1SbnbmpoafPnyBVeuXJEaXQwLC4O+vj40NTWhrq6OyMhI8Pl8VFRUICIiAlZWVpgyZQrH80n0O2jAiBEjEB8fD0AwDu/fv4e6ujoYhsHEiRM5bYWu0j179pTK26n09HSMHDkSkydPJs8qKysxffp0dO3aFTNnziTkE/uQT6MuAtzN2oYNGxAaGorx48dj//79KCoqAgAsX74cDMNg6dKlEnWPtr4JQ5OESE1NhYyMDExNTTn57vbt2wcXFxd0795djMwAuAc02uYcIEjGP2DAAEKmpaamkpLSQvLp9evX2LhxI+Lj46kk0YRg6+GSJUvQr18/WFtbY86cObh27RoAQQhubeQTQCeBERQURPKsAAKPZIZhoKqqiuPHj5PnT58+RWBgIBQUFLB7926x76H9ULVkyRJMnjyZk6sKEBQnkJeXR0REhMS8dzTqIsD9vRMTExEYGAhvb2/s3r2bkGjbtm1Dy5Yt4efnJ/E7aNPH+fPnc/7OyMiAiYkJjIyMOJeSp06dgo2NDXr37k3IUTZo65ck3L59G+bm5rh8+TJev36N5ORkMAyDGTNmkHX5wYMHmDNnDmbNmkU1CcrWxYSEBIwZMwZ9+vTB5s2bUVFRgaqqKqxfvx6mpqYc70g2aOvXokWLOHlZt23bBktLS5iYmHDOKDdv3iTkk2iaBoBe+yENqCOe6kAVjh49io4dO0JTUxPnz58nz0tKSrBkyRJSPla0qgrtm6PY2FgoKyvj0KFDuHz5MpYtWwZzc3M4OzuTxWjdunVgGIa43dKKvXv3iv3+jx8/Rt++fdG8eXMx9+fc3FwYGRlh4MCBv1LM/xjsjbroDZyQfFq8eLHY52hbaEURExMDFRUVHD16FMeOHUNCQgIaNGhAQkbKysoQERGBNm3aYOXKlb9Z2m9j+fLl5KZNmPPnzp07MDU1RZcuXWp1lQ4JCfnlsv4nqKiogI+PD5o2bSqWx6OyshKhoaGwsbFBYGCgWP4I2hESEgJlZWX4+fnB0NAQxsbG8PPzI4fmlStXomHDhmL5P2jDrl270LlzZ9TU1BCy6MyZM/D19UXjxo3FyM59+/bB3d0d7du3py6081vYvn07unfvDg8PD46u7d69W4x8YpNmtNvFGTNmQFlZGdHR0Rg2bBgcHBzQvn17Eipz9+5dqKiowNramvrxevr0KZydnTmHo4KCAkREREBGRgarV6/mtH/27BkmT54MhmFw8uTJXyztfwZhdSkbGxsO0QYIbIeSkhJCQkKov9ATxbRp06Cqqophw4bBy8sLSkpKGDduHB48eAA+n49t27ZBW1sbXl5ev1vUbyIjIwMDBgzgzP9jx46hZ8+eaNq0KbZt28Zpf/r0aXTv3h12dnaE2JYWREdHIyAgAAEBAZznW7duBcMwCAsLk7g+024bp02bBjU1Nfj5+WHAgAGoV68eRo8ejZcvX6KyshJr166FpaVlrRUkaUFqair8/Pw4Ml6+fBmOjo6Ql5fHhg0bOO1v3bqFTp06wdramqxpdfjPUUc81YEqvH//HlOmTIGqqqpY7oSSkhLExsZCR0dH4oGfVpSWlqJ3796IiooizyoqKrB371507twZERERnPwl0sKkL1iwAFOnTiWyP3v2DE5OTtDU1BTLA3H79m2qFyRJ+Pz5MyIjIyEjI4M1a9YA4B6k4uPjwTAMtm7d+rtE/GlUV1fDx8dHLHFidnY2GIYhpdBLS0uRnJxM7ZiJEs3r1q1DcHAwScTPvq36EVdp2iDJy6WgoACjR49GmzZtsGLFCo5XU2VlJcaOHQt/f38qPWRqw4kTJ6Ctrc0p+x0fHw87OzuMHTsWZWVlAAS3lN26daO6b5WVlUQv2TqXm5sLOzs7aGtrc7yeAIEXW1BQELXzTBKWLFmCTp06QVNTk1P4AhAcMt3d3WFqakptLiBJuHPnDgwMDHD48GHyLDc3F4MGDYKpqSkJibx16xbc3Nyov+hiY9u2baSiZ0FBAcLCwlCvXj2kpqZy2j169AjLli2j2jbWNv8jIiLAMAzi4+PFig4sWLAALi4uVNsOUZw9exatWrXiJCxOSUmBsbExpk6diurqahQWFmLdunXw9fWlWh9LS0uJfFlZWWQccnNz4evrC1tbW2RmZnI+c+TIEepzSkrCvHnzwDAMrK2tibe4sL/btm1Dw4YNxXJo0o6cnBxoamoSAh4Q7BfV1NSIV3lxcTGWLVuGUaNGUT9mQvn2799P1q/bt2/D2dkZTk5OYrp4/fp1DBkyhPp+SRPqiKc6/DaITmShgf748SOmTZsGMzMzREREcNoUFxcjNTWV6o26pA2OnZ2dWPUYAPDz84OXl5fYZ2jc/ImOlzA8cN68eWLkk5aWlsSDB63jVtuiUlxcjNDQUIkbdUBwy0/jWAkhqldFRUVo3749Zs+eDUDQb6HH0IQJE+Di4kLCnISgccxEc/4EBATA2NgY4eHheP/+PQBBOJA0ukqzdfH58+fIz8/HmzdvAABfv36Fn58frKyssHr1arG8CqK/C+1IS0uDlpYWh8AoKyvDwoULYWRkxAmRkZa+CavyRUZGkme5ublwdnaGnp6eGPkkBI3zrDa7uH79enTs2BGDBg0SC6Hetm0bgoKCpGqjfuXKFTRp0oTjZQ0IEhsbGxsjOztb7DPS0L/8/HzIyMigR48exOOnqKiIrGnCQhmioNE2itrFBw8ecDycpk6dioYNG2Lt2rVi5JO02A4hhIT8w4cPxUKSGzdujDt37gAAIeYBOvVRNI+OtrY2hgwZQp6dPXsWffv2hZ2dndiBX9J30ITa5FqxYgUYhhHzKgQElU1pv0ARxYkTJ6Cjo4MXL16gpqaG9DszMxP169cnNrOiooLaPKAAV6acnBx06NABI0eOJKT89evX4eTkBFdXV6nTRWlDHfFUh98C9gTesmULZs+ejRkzZpDSxJ8/f0ZwcDC6dOkiRj4JQftG/fnz5+Tv0NBQ2NnZ4dq1a5w2sbGxsLOzE9so0Qa2zA8fPiTuwikpKahfvz7mzJlDFp3nz5/DxcUF9evXJwdmmsHuW2pqKmJiYjBnzhxcv36dhBROmzZNqjfqT548IR4yM2fORJs2bXD9+nVOu9DQULi5uf16QX8S7H49fvyY/HvGjBmkDC6bfJImV2n2hnTu3LkwNTVF69atYWRkhM2bNwMQeC0MHToU1tbWiIuLE0v8S+umVtKm7fDhw2jbti0JqRC2yc/PR4MGDcRyztDYN3a/hEn4V65cCRkZGSxYsIC8y83NhYuLC9q1a8fRW1ohemi8evUqxzMtMTERtra2GDp0KF6+fPnd76AFbJmE/379+jXMzc2xZs0asSTobdu25ZCINIPdN+Ge4tatW9DW1oarqyuHfJoxYwYaNmwoFl5CI9jzftasWejcuTMaN24MR0dHTtj01KlTISMjg/Xr11Ofn1CI2uyivLw8bty4AeBvgqm6uhqtWrUiawHNEO1XUVER1qxZAwsLC05uqrNnz6Jfv35wdHSUeLlHI9h9e/nyJe7cucMhZebPn4969epxcqGKgkZ9lKSLZ8+eRf369ZGTkwPgb4KpsLAQenp6YqGS0tAvPp+PxYsXo1u3bhg9ejQhn65duwYXFxd4eHhIjS5KI+qIpzr8VgQHB0NdXR3Ozs6wtbUlFSAA4NOnT5g6dSqsra055StpBdu4zZs3D/b29sRV+smTJ2jdujV8fX1x7tw5VFZWorCwEI6OjhI9oWiCaL98fX051Zc2b96MevXqccinx48fY9KkSVSSg7UhODgYKioq8PT0RMuWLdGxY0fMmTOHbOBDQ0OlZqPOHrPw8HD0798fhw4dAgCcO3cOnp6e8PT0JGRMSUkJXFxcMGrUqN8i74+C3a/58+fDxsaGE9Y0ffp0MfLp6tWrUucqPX/+fCgrKyMjIwObNm1CSEgI6tWrhyVLlgAAvnz5gmHDhqFt27Zi1ahoBPu337RpExmzwsJCtGnTBl5eXmS8AAF5bWRkRC4iaAW7Xzt27MDhw4dRXl5OihDUq1ePQz5dunQJnTt3Rv/+/X+HuD8M0ep1urq6aNmyJZSVlTFixAiSnDU+Ph7dunXD8OHDa/Xkogns8Vq5ciXi4+OJh+ewYcPQvn17HDp0iKxbBQUF6NKli9TZ/ISEBCxbtozkorp9+zY0NTU55FNxcTHGjRuHbt26/RZ5/wkWLlwIZWVlZGVlYf/+/Zg1axb09fU5e6jQ0FAwDIO9e/f+Rkl/DOwx27x5M6c4gbe3N3R1dTmVjt++fQt9fX3s27fvl8r5s2D3a/369Thw4AAAwT4jLi4OnTt35pBP586dg6OjIyZMmPDLZf1ZiJKgRkZGaNy4MWxsbDBz5kxCEs6fPx8NGjRAUlLS7xL1pyDqDLBp0yZyoTpkyBAYGBgQIhQQnM/at28vsSABTWD3KykpCRkZGQAE47hkyRJYW1tzyKfr16+jc+fOmDJlym+R938BdcRTHX4bDh06hObNm3MSCCYkJKB+/fqkFPqHDx/w559/SlXukhkzZqBly5bYuXMnp2LH/fv30bFjRxgZGUFPTw+WlpYwMjIiHgu092/GjBlQVVXFnj17iJEWYsOGDWjQoAEn7E4IaSCf9u3bB01NTVy9epU8Cw0Nha2tLRYvXoyamhoUFhZi/PjxUrVRDw0NhaqqKrKysjjhTAcOHIC7uzvk5ORgbW0NIyMjdOrUiWpdFD0Mq6urIyMjQyykMzQ0FKampggPDxfzuJMG8qmoqAgODg6Ii4vjPF+zZg0YhiGb+K9fvyIyMpL6+cUet9DQUGhoaCA6OhqfPn0CILCLqqqqcHR0RFJSErKzs+Hm5gYzMzOq+yaqjy1btsTmzZuJbSwvL8fq1atRr149Tn6/27dvS4UeAgJyRkVFBefPn8eVK1eQnZ0NZWVlTnh4XFwc2rdvLzVeQYDAg7Vly5ZYsWIFJ5zTzc0Nbdu2xZ9//olFixahR48eMDIyotKjlQ22Lk6bNg3NmzdHSkoKxxPt1q1baNmyJdzc3Aj5VFpaSqWtl4SvX7/C1dWV5FsEBMTgpk2b0L59e469ZFdSpBWiY9a6dWsOWXjz5k1YW1tDXV0dycnJ2LRpEzw9PWFqaipVdrFFixZYuXIlh/CMi4uDiYkJhzD866+/pMYuAoK8gyoqKti9ezfOnDmDyZMnw8rKCiNHjiSe8tHR0WAYptbQLVogqotaWlpISkoiupiTk4OePXtCQ0MDa9euxcaNG+Hh4YHOnTtLlS5qamoiKiqKrNF8Ph8xMTGwsrKCv78/eS4a4lqH/1vUEU91+G1ITU2FiYkJJ/kgIKi6JS8vT6qjFRQUSE2Mfk5ODrS1tXHmzBkAArfUt2/f4tChQygqKkJhYSH27duHBQsWYP369dSWmRbmThCOy8mTJ6Gjo0NIwvLycrx48QL79u0jhIawbOy6det+i8z/CdatWwcDAwN8/fqV6FhpaSnGjh3L2ehJ00b96NGjaN26NfLy8gAIkh+/fPkSJ06cQFlZGUpLS7FlyxbMnTsXcXFx1OrizZs3OX+fOXMGenp6JNllZWUl8vPzkZ2dTfQ1LCwMWlpapEIkzWMmKtv79++hqqpKDlJ8Pp/k4vL19cXYsWPFwoFo3vwJsXjxYqiqqiIvL4/IK/z/ixcv4OrqCn19fRgaGsLDw4OQoDT2jb1eLVmyBC1atEBubi7nuVBuYVW+0NDQWr+DVgwbNgyTJk3iPLt79y6aNGmCsLAw8iwjI4PKcZKEpKQkqKmpcUJv2ZWmFi9eDG9vb3Tr1g0jRoygWg9FsXbtWmhoaBCbDwj0TOhNeOvWLWhpacHMzAwFBQWkDY32UVSm8vJyGBgYiFUkLS4uhre3N/z9/cW+g7a1TBKWL18ONTU1TiJxId69e4fRo0ejbdu2MDU1ha+vr9ToY2xsLNTU1HDt2jXyTDgewkpopqam8Pb25nyOdrvI5/Px9etXODs7c/I4lZWVIS4uDqampti4cSN5npKSIhV6CAjyU6mrq3NCqoV49uwZJk6ciBYtWqBLly7w8fGRGl1ctmwZVFVVJeoiAKxatQo2Njbo27cvx8OQdl2UVtQRT3X4JZA0gXfs2IGGDRuS5KRCI3bnzh1oamoS8uZb30EbDh06BH19fXz58gW5ubmYPn069PX1oaCgABcXF7GS2gB9RjsiIgKurq6cZ2fPnoWhoSHu3LmDW7duYdq0adDR0UHr1q2hqalJbkYOHjxI/SIrSY+Sk5PRtm1bskEX6uKLFy9Qv359HD9+nNOexo26KI4dO4aOHTvi6dOnuHPnDsLCwqCjowNNTU3o6+sTjxM2aNPF+Ph4zJgxA8Df45aZmQktLS0AghxOM2fORNu2bdGgQQM4ODgQ/YuLi6OuP6Jg6+LTp0+J3o0YMQJOTk54+vQpgL/1bejQoRg4cOAvl/M/RWlpKfr370826k+ePMGePXvg5uaGadOm4enTp6iurkZ+fj6eP39O+kubLZk6dSoJpeDz+aioqICvry/JQ/j06VPs378fvXr1QkBAAO7duwdAcPNNe1JZUbtYUVEBKysrjleCkPCMioqCpaWlmA2hfb4BAq+78ePHAxB4261btw5GRkZwcXHh5M5hk1G06SEADB8+HJcvXwbwt30ICgoiyZsfPHiADRs2wNzcHGZmZtiyZQsAQSgJ7ZXQRO1iYWEhAMDf3x+9evXCo0ePOO1DQkI4ZLW0oKysDP369UN0dDQAwZjt2LEDPXr0QL9+/Yg375s3b1BYWEitXQwJCSE5IwGBHRg2bBjxgnzy5Al27doFe3t7jB8/HqdPn0Z1dTWWLFmCESNGUK2LgPh+r7q6Gl26dBG7TAAAJycnDB48WOw5bWPGBp/PR2VlJfr27Ys5c+YAEHj97Ny5Ey4uLvD29ibek2/fvkVJSQm1uhgVFcWJXKioqMDw4cPJHBP2q1u3bhg5ciQh2cLDwzF27FjqdfHfgHq8OtThvww+n8+rV0+gatu3b+dt2bKFx+PxeC4uLrxu3brxJk6cyHv+/DmvYcOGPB6Px2vSpAmvSZMmPD6fz/ke4XfQAgBiz0xNTXkvXrzgubm58VxcXHhfvnzhzZ8/n5ednc3Ly8vjPX78WOwz9evX/xXi/jB69+7Nk5OT471584Y8k5WV5TVu3Jg3evRonq2tLe/Lly+8iIgIXmZmJq9Ro0a8nJwcHo/H43l4ePAaNGjAq66u/l3ifxNsXUxJSeEdP36cx+PxeD179uR9+vSJN2PGDB6PxyO6WFBQwGvfvj1PRUWF8z0Mw/xCqb8PSbpYv359XsOGDXnDhg3jdevWjffhwwfenDlzeLt27eJVVFTwzpw5I/EzNEFVVZWXkpLCe/XqFRk3MzMzXsOGDXkdO3bkOTs78z58+MALDw/n3b9/n3f69Gnevn37eDwejzd+/Hhe/fr1eTU1Nb+zC7WCrYsRERG86dOn87Kzs3k8Ho/n6urKKysr4y1fvpz36tUrHsMwvPLyct7r1695mpqav1Psf4SGDRvynj9/ztu3bx/vwIEDvAkTJvCWLVvGk5eX56WmpvKio6N59evX56mqqvK0tbV5DMPw+Hw+r0GDBr9bdA4uXrzIO336NI/HE4xfTU0Nr6qqivfkyRNefHw8b+LEibxVq1bxysvLeffu3eOFhYXxqqqqeFOmTOGdOXOGxzCMxLn6u8HWxcuXL/M+fPjAk5GR4Q0fPpx3/PhxopcyMjI8Ho/Ha9y4Ma9+/fo8OTk5zvfQZj8koaioiLdt2zbekiVLeEOGDOHt27eP5+HhwWvatCkvISGB9/XrVx6PJ+gjjyewrbTpIY8nWIMOHTrE4/F4ZK/UtGlT3uPHj3lBQUE8Pz8/3sGDB3nW1tY8Gxsb3syZM3mvX7/mmZiY8LKysnj16tUT22PRALYuzps3jxcQEMC7ePEij8fj8YYNG8Y7c+YMb+nSpbxbt27xeDwer6SkhHfp0iWenp4eWbelBY0aNeLJycnxtm/fztu4cSNv7NixvA0bNvDat2/Pu3v3Lm/YsGE8Ho/Ha9GiBU9eXp5Ku/jlyxfe3bt3eeXl5Twej0fW2xcvXvAOHTrE27x5M2/MmDG8pKQknoaGBu/y5cu8+Ph4Xv369Xnjx4/nJScnU6uLPJ5AH4X7vadPn/IqKyt5AHh6enq8vLw83ocPHzg23dramldYWMirqqrifA9NYyYKhmF4DRs25KmoqPCOHz/OW7lyJW/MmDG8jRs38rS0tHj5+fk8b29vXk1NDa958+a8Jk2aUKmL79+/5x06dIhXUlJCnsnIyPA+fvzI27ZtGy89PZ03ZswY3rp163gdOnTg5eTk8BYtWsTj8QS2JiEhgWpd/Nfgd7JedfjfQkhICFq1aoWVK1eS3Ctbt26Fk5MTunfvjiNHjuDIkSPw9PSEpaUl1benbFb806dPKCkpIVVUHj58iPnz52P//v3kpq6qqgqWlpbUx3oDgiodurq6WLlyJef58ePHkZSUxOnX169fYWpqiv379/8OUf8xpk2bRuK98/PzAQhC0xQUFNCvXz8cPHgQOTk58PT0hJWVFdW3IGzZioqKSLJcQJC7avny5cjKyiLhk/n5+ejcuTOOHj36q0X9aRQXF6Nv375ITk4mz6qrq3Ht2jXMnTsXe/bsIf0qKiqCtbU1Tpw48Zuk/WeYOXMmVFRUsG/fPk5OqhUrVsDa2ho6Ojro1asXLCws0LFjR3LDSKv3TG1z5eLFi2jfvj2aN2+OOXPm4Ny5cwAEoU3S4K1QU1ODgQMHYvLkyZznW7duha2tLdTU1BAREUFCQGfPno0BAwZw2tI4ZmyZwsLCYG1tjYSEBFRVVeHWrVsYNGgQ7O3tSULjT58+wd3dHYMGDaKyP0J8y2YPGjQIlpaWiI2NJaG8x44dg6WlJScXHo0Q/uaxsbHo0aMH592tW7cwbtw4WFpaYsWKFbh16xYAYPfu3XBwcOCE19GO2bNno3nz5sjIyOAUHjh8+DC0tLTQpUsXWFpawtramvr8hEDt+nj48GH07dsXKioqmD9/PvFi27hxIzw9PYmHJc3w9/eHvb0959mTJ09gamoKPT09REZGEs+S1atXo0ePHpx+ScOYzZ07F927dyf7iwcPHkBBQQFDhgzB06dPUVVVhbKyMnTr1g0BAQG/S+QfQm26uHv3bgwePBjNmzfHggULSGqNxMRE9OzZkzrvJkkICwuDo6MjKYABCMbK1tYWWlpaiIiIQG5uLgDBHHN0dKQ+7PjfhjriqQ6/BBs2bKg1djgjIwN9+/ZFw4YNYWxsDCcnJ6pjh9lGOzo6Gs7Ozmjfvj0mT55M4vSFbcrLy8lG3cLCgsr+sCE0usnJyWjVqhWnYhgblZWVePfuHby8vGBlZUV9v9hITEyEqqoqrl69KnbYzc3NhaGhIVq3bo127drB0dFRanRxyZIlcHBwgKWlJQm3YKOyshIfPnyAt7c3bGxsqOyPJEyaNAmGhoZkcyC6MaioqMD79+/h7e1NPWEtiqtXr6JDhw6cUE72mJ4/fx5LlizBmDFjMH/+fGrzcAnBlv348eOkgp2QUKuoqOAkPK6pqYGLiwv1G3UhLly4AFVVVbGKWe/eveMUkgAAd3d3iXlnaEVERARUVFRw8uRJkggYEORUGzZsGJo0aYL27dvD0NAQJiYmVB/02Xq4fft2hIWFISYmhkO2CwlrQGAb3d3d0atXLyr7Iwnl5eXQ1tbm5NoCBH1hXz5UVlbC29tbqvp29+5ddOjQQexCS2jbb9y4geTkZAQFBWHZsmVSZRf37duHjRs3YvPmzRx52UnuAcDZ2Zn6isdCfbpx4wYcHBywc+dOzvuKigpOIZqqqiq4u7tjxIgRv1TO/xRz5syBuro6MjMzOeN05coVqKiowMzMDBYWFrCxsUHHjh2lxjZmZWUhOTmZk5i/qqqKQ/QCgKurK4YOHfrLZPwnEP7WOTk5cHR0JEVY2GDrYk1NDVxdXTF8+PBfJWId/j/qiKc6/FchNAZjxozB2LFjOc9ED/337t3D27dviWGkdRMhhNBTYfPmzUhISED37t1hYWGBnJwcAIJFd9WqVbCysoKVlRXVBIYoHj9+jH79+mHAgAHk1hQQGOuKigosXboUbm5usLS0lKp+AcD48eOJ14JQx9iLcUlJCR4+fIi7d+9KjS6GhYWhZcuWiI2NRWZmJpo1awZfX1+SP409Zl26dJGKMWNv2gwNDeHs7Ez+Fo5HRUUFtmzZgu7du6Nr165S0S82zp8/j5YtW4olUAcEOilJ72jtm2j1ulatWsHQ0BAGBgYYPHgw8QQCgMLCQuzfvx+enp5SU9mTz+ejpKQEo0ePxoABA3D79m2xNl+/fsXx48fh6emJTp06Ue+dJsTLly9hbW3NOTiyZc7Pz8e5c+ewfPlypKamEh2k0S6KVmhq2bIlevfuDWdnZ5ibm2Pt2rXkfUFBAdauXQt3d3cYGxsTPaTZwxX42wYkJibC0tISO3bsIO+E/S8sLER6ejrc3Nykqm+AgJBv0aIFKTDDHtPy8nJO/i0hpMUuqquro1u3bpCXl0evXr1w+vRp8r6wsBCHDx+Gs7MzZ8xotx9Cz2Q3Nzc8f/4cAFdmoS56eXlJhXcaGw8ePICBgQGysrI4z4X69vr1a6xevRqhoaFYsmQJ1SSopAqz3bp1g7q6OhwdHZGXl0faFBQU4NixY3B2dpaaNVoIX19fWFhYkMsF9lgUFxdj165d8PDwkLp+/VtQRzzV4Zegd+/eEm9vysvLcfToUbFJT/vmaO/evTA0NCQeTkeOHEGjRo1gamoKExMT8vzatWuIjY2leqNeG9LS0mBtbY3Ro0dzKgABgrC0lStXSl2/qqurYW1tzUnQLNS98vLyWgkAmnHw4EEYGhoS77Ts7Gw0bdoUioqKsLa2Jh4m+/fvx9KlS6neGIlC+NufPXsWmpqa6NWrF+d9aWkp0UVp6pcQ2dnZUFBQIEUH2NXqjh49irS0NOr1TxRLliyBpqYmCaWbOXMm5OTk4ObmRkj5K1euwM/Pj1MZR1rGbd++fTAxMUFwcDBJ/itEbm4u3Nzc0KdPH6kiQV+8eIHmzZtj9+7dYu/Ky8s5HlBC0N6v+Ph46OrqEsIzKSkJMjIy0NHRIUnuP3/+jNDQUPj5+Uml/Xj06BH69+8PDw8Pzg0/n8/HixcvMGHCBPj7+0td327fvo0GDRpgz549AP6u7AkAJ06cQFZWllhlT9oRGxsLLS0tEkonrALs4eFByKcLFy5g/Pjx6N+/v9SMmXD/9Pr1a6ipqWHAgAEcb0I+n4+HDx/Cz88Pffv2lZp+CXH58mWoqanh8ePHAAT9Efa5rKxM4vpMu21ctmwZNDQ0SBLunTt3gmEY2NjYEP28cOECRo8ejUGDBknNmAnHoqCgAHp6enB3d+ecLfl8Pm7evIkJEyZIpS7+W1BHPNXhlyAkJAQaGhpi4Qhv3rzBkCFDxCrY0Y4rV65gypQpAAQHehUVFaxduxbHjh1DixYtYGZmhlOnTnE+Q+NiJInlZz/bsGED7Ozs4OrqKub2LjTWNPbrW4iMjETXrl3JIViI+/fvo2fPnmIkG+04ePAgli9fDkBAZCgrKyMpKQl3795Fs2bN4OPjQzyfhJC2MausrMSePXugpaUFKysr3Llzh+RUY28apK1fANCtWzcYGRlxDlKlpaVwd3fH7Nmzf6NkP4/379+jT58+pJz0vn370KxZMwQEBKBz585wdnYmpd6fPn0qNR6FokhMTISOjg4mTZokVgb93r17Utevx48fo1WrVoiPjwfAnUdnz57F/PnzOeFbtKOiogKTJ0/GokWLAAB79uyBoqIiIiIiMHz4cLRo0YLoaGVlJVnzpNF+XLp0Cc7OznB1dcW6des47/Lz86Wub3w+H6Wlpfjjjz9gbW2NkydPkndVVVVwcnLCxIkTf5+A/wCfPn3C+PHjSa7CXbt2QVFREeHh4dDW1kb37t1x/vx5AALvQ1orhtUGoW5duXIFCgoK6NWrF27cuMHRvffv35N20qKLgGCdUlRUJFUhgb/lP3ToEPbu3StVl0P5+fmYOHEi6c/u3buhqKiIFStWQF9fHzY2NoSQornCbG0QjsWlS5egqakJJycnPHnyhMhfVlaGt2/fSt2l+b8JdcRTHf6rEBqt0tJSdO7cGaamprh9+zbevXuHt2/fwt3dHba2tlQvRLUtKh8/fkRZWRmcnZ1J2VhAcJDU0dGhPnaY3a9nz57V+u7QoUOYMGECZGVlMXPmTLE4fmmDMMmxn58fyfnx4sUL+Pj4oHv37lKnizU1NXj27BmKi4vRvXt3hIeHAxDEsxsZGYFhGPz555+/WtSfwo+4OVdXV+P+/fuwtbWFubk5AgMDkZOTQ3VS6m/1S/ju/PnzMDExgY6ODpKTk7F69Wq4urpyQrWkCTk5OXjz5g3y8vLQqlUr4l0SGRmJJk2awNzcnFN6m8ZNe202QJSUt7e3h52dHTZs2CDWlsZ+fQthYWFo3LgxJ99YSUkJPD09MWLECKpDESTJ9vHjRzx69AiPHj1Cu3btsGzZMgDAgQMH0LhxYzRp0gSpqanf/A4a8CN6lJeXh4CAAOjr68PPzw/Pnz/nEIW09u1bOHr0KHr16oV27dphwYIFWLRoEXr06AEjIyOps4sVFRUkd9r169ehp6eHFStWABDkIJORkYG1tTWuXbtGPkPjmInqIvtvoby3b99GmzZt4OLigujo6G9+hnbw+Xx8/foV/fv3h5ubGw4dOkTeVVdXw9nZmaQQkRZUV1cjOzsbHz58wPXr19G2bVtSSGjbtm1gGAZt27YlYa4Anbr4I/v0mzdvolOnTjA1NcWqVas4OZ4A6dLFfxPqiKc6/J/gRwzTw4cPYWNjg+bNm0NLSwumpqYwNzenOvcAW6YHDx4gNzcXb9++JRufZ8+eQVNTE9u2bQMguPEfOHAgdu/eTaWxloQZM2agX79+YuEUbPmrqqpw9OhRDB06FDY2NpgzZw4+f/78q0X9Ln70Nz927BhsbW2hp6cHLS0tGBsbw9TUVGp08fbt27h37x4n/9bz58/Rtm1bHDt2DIAg38zIkSNx+/Ztqsk0du6fhQsXcnKw1Ib169cjMDAQWlpaWLt2LV69evXfFPG/ipqaGjx9+hR//PEHOnbsiC5dumDw4MHUh2p9b44sWLAA3t7eKC8vBwDExcXByckJ8+bNo3J+CcGucMPefAvBlv3ixYtYuHAhlJSU0KdPH4SEhEjMP/O78SMEaEFBAUaPHg2GYeDv708qVUlTstyKigpUVVVx5ExNTYWZmRlZr44fP04qZdI6tyQhJiYG27dvr/V9fn4+zpw5AwsLCzg7O6Nbt25U2v7vycMeu8uXL2Pu3LnQ0dGBo6Mjhg0bRn1orqhtE/WsWLlyJRwcHIg+btiwAf369cPw4cOptotsIpPthcaGUP73799j9uzZsLe3R7t27RAVFYU7d+78CjH/Kzh9+jRcXFzQpUsXhISEICYmBvb29tSToLXpovB5fHw8HB0dCSGzbds2TJw4EX/88Qd1dqM2JCQkfLcSaXBwMHx8fKCpqYmlS5dyLr7q8OtRRzzV4T+CMAb/Z5CRkYGUlBTs2rWLandH0TLTJiYmUFJSgouLC4YNG4bi4mIUFRXB29sbbm5u2LRpE1xcXODg4EAMO40bCXa/Tp8+DTMzM7Fwkdo+U1FRgfLycty7d++/KuM/wc/K9PjxY5w7dw6xsbHIyMiQGl2cM2cOjI2N0a5dO2hpaSEqKgo1NTUoKyuDtrY2fH19sXv3bjg5OcHa2proII0biVevXkFWVhZ+fn6YOnUq5OTkvrlBFe2DsCABbZg0aRIOHz7805/7+PEjSkpKqHdvZ9u1devWISwsDH5+fjh06BA5UM2ZMwempqaEvOnVqxdWrVpF+kajbTx8+DDGjx+PoqIiTJgwAa1bt5YYYiZKwLx69QopKSmYP38+dWHjP5sLJzExEQMHDoSvry+Cg4OpzoMhWtVz8ODBMDU1xYIFC0hIZ0ZGBlq0aIH09HQUFhbCy8sLkydPpj4Ejd235ORkaGhokPwr38Pt27exbds2Er5FCzw9PWutlMuG6PwqKSnh/E2jLgLcMUtMTMTEiRPh5uaGjIwMsk7NmTMH5ubmuHfvHkpKSuDj44PExESJ30EL0tPTMWjQIFRWViIoKAiqqqpi3iNCsMkNPp+PdevWYePGjbWSVdKCK1euICIiAvr6+nB2dsbw4cOpJkFFdXHs2LHw8fHBpk2byOXI9OnT0b59e7x+/Rpfv36Ft7c38X4C6LSN7H6tXr0aDMPUSiSx5S8uLsbWrVuRkJAgsbp6HX4d6oinOvxjLFiwAH5+fhxD8K1FszYjRqNxYyMmJgaqqqo4efIkKioqMHLkSMjJyZFNXXp6Otzd3dGuXTu4ublR7TXDRnJyMsaNG0dKftMu77cwe/Zs2NjYAPjPbuVp18WoqCioqKjg9OnT+PTpEwICAjgL7/nz56GjowMjIyM4OjpKhS7m5OSgYcOGkJeXJ0m2aR+Hb+HFixcYN26c2Gb0R7xO2G1oHjMhpk2bhubNmyM0NBS9e/eGvr4+goKCwOfzkZmZCUtLS+jr65PqdrRXeYuNjYWRkRHMzc2hoqIi0eNJFDTr6ujRo7F06dKf/pyo7tJ4sGJjxowZUFVVxbp16xATEwNjY2PY2NigoKAADx48wJAhQ6CkpERsI80eXKK4cOECgoKCSP6mb8lMsy4CAkJeSIT+6G/PTiwOSIddnD59OjQ0NDBlyhSEhoaCYRiEhYUBAG7cuAFlZWXo6+tLjT6eP38eDMPAyMgIioqKuHHjBgB65f0RODs7i+X5/BFUVVVx5hnttnH69OnQ1NTE5MmTsXDhQjAMg1mzZgEQXJg0b94cLVu2hK6uLkcXacepU6cQFxeHzMzMb7YT1VFp1tl/C+qIpzr8Yzx79owYXeHtIvD9jYHwPa0GgH0ILC4uhpeXF0lEeujQIcjJyZFNoLAvlZWVePv2LfWeCmwMGjQIDMPA3NwcX79+BUDvmHwPnz59Ir/5mzdvAPzYBpX2RYktT0VFBXx8fEhekszMTCgpKSEhIQHA3zpXWlpKfVJI9thcunQJDRs2RNOmTTFixAiJbQD6xkYSRGVMSUlBSkpKre+/9VnaD5EHDx6Erq4uSUSanZ2NBg0acMKBDh06hGXLlmH+/PlUFyNg65qvry8YhsGQIUNqvdWXBNr0s7y8HPHx8WIhmz9iFyXlbqEVeXl56NixI7kIOnr0KBo1akQSOQOCilvHjh3Dtm3bqPZsZYPP5yMvLw+ysrKQkZEhOaqkEaI6t3TpUmRmZv7QGNCuf6I4duwYWrduTexiXl4eGIYh6RgA4NatW1izZg3WrFlDtUchn88n82XgwIFgGAbe3t5kvyjNmDt3LgkD/1FIW/XtkydPQkdHh3j4CAnEzZs3kzbv3r1DbGws1q5dS7UuspGbm4t69epBVlaWEE+0j0Ud/kYd8VSHfwT24WHPnj3Q19f/IXdhtuH+9OnTf0/Afwi23O/evUNFRQUcHR2Rk5ODffv2QU5Ojhz0KyoqkJSUhOPHj1PvqVDb5k3oNr1s2TKp3EywS9sCgmoxDMOQ0MFvjQX7c8+fP//vCfkPwZb9yZMnqK6uRvPmzXHq1CmcOHGCo4vl5eWYPXs2J1+S6HfQiMzMTLx9+xbv3r3DqVOnoKioiD/++ON3i/WPwOfzORu2kpIS2NjYwN7eHunp6Zx2kj4rREpKilgVQhogShht2bIFDg4OAIAdO3ZAXl6eVEUrKCjA5cuXpY5MKysrQ1RUFMLCwmBhYYHAwEBSRpv2ucSG6O++fv16jBkzBmVlZQB+3C7SCFEdunTpEtq1awdAYP/l5eWJXSwqKsKuXbvE1jba9ZCNLVu2QFlZGd7e3sQjVNrh7OwMRUVFZGdnf/OQy9bF7du3IyMj41eI91MQnUu7d++Gi4sLAIHMcnJyxC5+/fqVk0BcCNr1sby8HFu2bMGmTZsgKyuLoUOH4uXLlxLbSpOdBIDFixdj7969P9SWdtsoSRednJwACCIz2HvGL1++SAzdpV0XAeDt27eIjY2FkpISJk2aRJ5Lm+79r6KOeKrDT4M9uZ8/f443b95g6NCh6N69Oycx8Lc8FpYvX47OnTtzErn+brDlmzBhAvr27Ys3b97AyckJ3bp143iXAAKPLxcXF45HA41gj8PTp0/x4sUL4hUEAKNGjULbtm2RmJhI1Xj8CNj5S758+YL8/Hz07dsXysrKZFGVtBixxzo+Ph4+Pj5U5goCgNDQUPj6+qK4uBgTJ06Er68vmjRpgvXr15M2r1+/JnnGpAF8Ph+5ubmQlZXF69evAQjG6eDBg1BUVORUhBw/fjy2bt36myT9cTx58oT8Oy4uDvfv38fDhw/h5eUFJycnTjVItv6x/7127VowDIMDBw78GqH/ARITE/Hx40ckJibCx8cHp0+fhry8POLi4kibnTt3Ijg4GPn5+b9R0p9DdHQ0ZsyYQf5esWIFTE1NERgYyBlbtncvrRC1eTNnzoSZmRmmTZv2TfKJrYuJiYnfTGb9uzF//nxkZ2fjzJkzsLS0xKZNm6CgoEAO+YDgxt/Pz08qEhuzx0P08Ld+/Xq0bNkSwcHBhAiVFrCJltWrV5P507t3b6ipqeHgwYMSySe2LiYkJEBBQeEf5c37VViwYAHu3buH3bt3o2PHjkhPT0ezZs04+piWloZBgwbh/fv3v1HSn8OqVaswatQo8vfZs2chIyODoUOHkrUbwHdDnmgEn8/HgAED0KhRIxw5cuS7bYXYtWsXddWd2fLFxMTg4sWLOHLkCDp37oz169eL6WJmZiY8PT05Y0gjaiOTPn36hJiYGMjIyJBKzt9qXwd6UEc81eGnkJaWRozXlClTSF6de/fuYfjw4bC2tpZIPoluaJWVlTmux78bbPlevXoFc3NznD59GoAgJl9DQwPdu3cHICA7vnz5Ak9PT9jZ2VF9Q8Du1+zZs2FqagolJSXY2tpyjPXIkSPRrl07rF27Fl++fPkNkv48jh49iiVLlgAAxo4dCw8PD1RVVeHdu3cYMGAAmjVrJpF8Yv8mSUlJaNKkCccj5XeDLevZs2dhbm5OPLg2bNiA5s2bw9fXl1Qh/PjxIzw9PdG9e3ep0EXh/4uKiqCnp4dz585x2gjJJwsLC9jY2KBt27bUu35fv34dDMMgMzMT06ZNg4qKCh48eABAUBnN3d1djHwSJl8VIjExEc2aNaPuVp+tjytXrgTDMHj06BFevnwJFRUVMAzDISjKysrg4eGBkSNHUn9DLERNTQ38/f3FSmOvWLEC5ubm8Pf3x+nTp+Hq6gpzc/PfJOWP4eLFiySEZM6cOdiyZQvKysoQHh6Orl27YurUqRLJJ0kE6K5du36t8N8AW9adO3eiYcOG+OuvvwAA9vb2YBgGy5cvJ23Kysrg6emJ3r17U38YYcsXHx+PYcOGwc/PDwsXLiTPExISoKGhgeDgYA4RSjNu376NNm3aYMaMGQgODkbDhg1x+/Zt8t7Hx0ci+SSaGFlBQYGqNRrgzpdNmzZBTk4O586dw4cPH+Dk5ASGYbB48WLSprS0FD179sTQoUOlxi4CwJ9//glXV1cAf4/LuXPn0KhRIwwePBgHDx5Ez549YWBgQH2/rl27RvqwfPlyXLt2DXw+H6NHj4a8vDwOHTok8XOiF5Xy8vI4fvz4L5H5R8CeLxs2bIC6ujrOnz+Pp0+fwtXVFTIyMoiMjCRtSktL4ePjQ70usvu1efNmzJs3D2PGjMGlS5dQUlKCiooKxMTEQEFBAREREaQtzX2qQx3xVIefRGRkJBiGgZubG5o1a0Y2foDggCUkn4Q5kABuvLDwcEXLhlZIsggN3MKFC9GrVy/88ccfKC0tJQYsLS0NsrKysLS0hJWVFbp37w4TExPqS54LsWDBAigrK2Pv3r3YsWMHIiIi0KRJE0yePJm08ff3h7y8PLKysn6foD+IyspKDBs2DObm5ujRoweUlZVx69Yt8p5NPl25cgVA7Qf93bt3/3L5JeHmzZucv9evX4+xY8dybhsBwRzU19eHmZkZ3NzcYGVlBVNTU6nRRXaVsDZt2mDVqlVibe7cuYPRo0cjNDSU6txAQg/B0tJSLFiwADIyMlBQUMCzZ88A/G37hOSTs7OzxAMUbXZRCFESdM2aNRz7sHPnTqioqGD06NHIzc1FdnY23NzcOGWmadwESiIili9fDiMjIxQXF3M8KePj42FjYwMdHR3Y2tr+dJW4X4k3b96gXr16GDVqFMaPHw8FBQViV0pLSzF37lwx8qm6uloqCFAh0tPTsWHDBiQlJZFnT58+hbm5OQwNDREfH4+VK1fC2dkZHTt2JHpIO/kECLxbVVVVMXnyZPTs2RPt2rVD165dyfukpCRoa2tj9OjRVHsqCD163r17h+XLl0NZWRny8vKk8qywqhYgIJ+aN2+O7OxsscTGSUlJVNpFNo4dO4bJkydjy5Yt5FlycjIsLCzg4eGBs2fPIi0tDe7u7lJpF1NTU2FtbY2ysjLU1NQQ+XNycqCjowNTU1NYW1tTnyD9+vXr6Ny5M2bNmoVJkyaBYRhOJeSRI0dKJJ9ESVBFRUXqSFAhcnJyMHbsWE4Op+TkZBgZGaFv377Yu3cv0tLSxNZo2m1jcHAwVFVV0bNnT3Tu3Bnq6uqYN28ePnz4gNLSUixduhQqKioICQn53aLW4QdQRzzV4buYNWsWp1xlp06dUL9+fcyZMwcAd6G5f/8+RowYgW7dunFuHwHBLSpNm4iIiAhykwMINuDLly9H48aN0alTJ7HEg48ePUJ4eDjmzp2L9evXS02S0sLCQri6unJCYUpKSpCSkoJmzZpxPNQWLVpE5QFfiAULFhBPNACwsLAAwzASF5x3795h4MCBUFJSEqteInTdp0UX4+PjSZiP8PcfPnw4GIaBiYmJWD60AwcOICYmBlOmTEFSUpLUJIWMjY1Fhw4d4Ofnh4kTJ8LLywuhoaF4+vTpNz9HY7/8/f1hYmJCwsk2bNgAhmE4niLs5Kz379+Hp6cnjI2NObelK1asgJKSEjW6CIBDTACCyloMw6Bx48bYs2cPef7161ekp6dDV1cXmpqaMDMzg6+vr9SQoJmZmcjJyUF+fj5WrVoFKysribr28OFDzm05bfp49epV8ptfv34dMjIykJOTIznfhONQWlqKefPmwcrKCiEhIRwCAKDvoD98+HBOHpKXL1+iWbNmYBgG8+fP57R9/fo1+vXrB3Nzc9jb28Pf35/qcueiyMvLQ+vWrYlt4PP5OH/+PAwMDEguNUDgdejr60vtAT8yMhKNGjXCw4cPAQBbt26FnJwcdHV1MXPmTNKObV969+4NhmE4OQrj4uLQtGlTai6GACAkJISzFz59+jSMjIygoqLCIeRramqwadMmuLm5oUmTJujatSv69esnNXZx06ZNOHHiBG7evIkdO3agefPmEkM8X79+jbt371JrFwEQcqmoqAjh4eFQV1eHnJwcuZBkk50jR45Es2bNJIZ00mYbo6KicPXqVWIHTpw4gTZt2kBVVRU7duzgtF27di369OmDRo0aoVu3blKli4cOHYKGhgYnZHf+/PkwMjJCTEwMAAHRHR4eDldXV2rtYh3+Rh3xVIdv4uvXr6hXrx7s7OzIzemff/6JMWPGoF69epycR8LF5/79+/D19YW/vz8xAlu2bAHDMFTdot64cQN9+vTh3ByWlJRgw4YNaNiwISHWgNoXVNqNNiDw6tLU1OT0BxAsxP3790dAQIBYP2js16VLl2BmZgYvLy+cOXMGADBs2DD06dMH1tbWWLp0qVgIyfv37+Hs7EySfQLAtm3bqNpAAAKPOg0NDbGEnWFhYVBRUUFMTAw+f/78ze+gccxEsXHjRiQkJGD8+PFwdnaGlpYWGIaBsbExunXrhv79+yMsLIyzuacVN2/eROvWreHs7IyPHz+iuLgY9+7dw/z588EwDLkBZ9uOJ0+eIDg4mIzVixcvYGJiQioV0gIbGxvOze/bt28RExMDRUVFiSRvSUkJ7t69i9evX1NdTZGNmzdvQllZGW3btoWqqiosLS3BMAxGjhyJ1NRU5OTk4NmzZygpKeF8jrZ5Fh0dDYZhcPDgQZSVleHy5ctgGAaNGjWCv78/CgsLAfxtE0tLSxEeHg4dHR2sXr2afM+yZcugoKBA1UF/xIgRHIKJz+fjzJkzMDY2hrW1NSHO2Df2X79+5Xil0aqHomkIDh48CGVlZU7+xaqqKmRnZ6NDhw6cHDTCz9DoqXDhwgW4u7tDV1cXT58+RXl5Of766y8sW7YMhoaGCA4OJm3Zc2nWrFnk78ePH8PV1RVpaWm/XP7a8PnzZ3h5eZEKYYBgLkVERKBly5bo2bOnmK0ABBeWxcXFUmMXr1y5gs6dO0NbWxtNmjSBra0tqWaXmJiIjIwMfP78WawgC426GBMTAw0NDbJfTE1NhaqqKjp27IjZs2eT/SJ7TP78808wDIPc3FzyLDExEU2aNKHGNr579w7du3cn/RIiMjISqqqq6NOnj8Scpc+ePeNEctCoi6Lr644dO6Cvr4+3b99y3oWFhUFdXZ0UjigoKBBL51AHOlFHPNWhVrAP761atYKNjQ1nsYmMjBQjnwBBwvGCggLOQnTlyhUcPHjw1wj+g3j58iV0dXWxcuVKzvPy8nLExcWhXr16iIqKIs9FQ7VoxM2bN8nGderUqeQGMTAwEN7e3pz8CgAwbtw4eHl5/XI5/yn27NkDd3d3eHh4cBLGjho1CpaWlli6dCnnFj8/Px9FRUUcXUxJSUF2dvYvlft7KC4uRt++fUkJcPYt3MSJE6Gnp4c1a9aQ0FDa9RD4drJc4bv58+dDW1sbOTk5iIyMxODBg9G/f3/qDve14d69e9DS0oKTkxPxfCoqKsLMmTPBMAyHUJo5cybHtZ/P56OyshLv3r375XJ/CzU1NRg4cCAnDBcQzKXo6GjIyMhwcs+IhscIv4M2SJKpsLAQhYWFOH78OPbu3Yt69epBS0sLlpaWaNq0KZSUlBAQEPAbpP059OrVC+rq6mSNraioQG5uLuTk5DBixAhOeCsgOHCkpKSQeVZeXo7+/ftTk3dRaN9iY2PRo0cPzruamhqcPXsWWlpacHd3J88l6SGtdlKYnw8Q5CoEBIdCXV1dTpgMICB91dTUxApH0No3QOCB5+LiAj09PTx69AiAIAw0OjoahoaGmD59OmkbGhrKSdkg7NerV69+rdA/AH9/f9jb23OeVVRUICoqCqampggKCiL7D0khddJgF4Xy5ufn49GjR8jKykLz5s3Ro0cPGBgYoHnz5lBTU0Pfvn1/h7g/haNHj6Jfv34wMzNDbm4uiouLcf/+fYSHh8PS0hLTpk0Ti2yorq7GokWLyPg9fvwYjo6OVF1UAgLixdHRUaxiZ0REBIyMjDBr1iwS8vq9Sro0Ijc3F9XV1di2bRvU1NSIzRTOr69fv0JJSUmsIiHt/apDHfFUh+9AuDF99+4dNDQ00K1bN04unQULFqBBgwZYsWIFHj9+DF9fX3h7e5P3NC60wN/GKTk5Ga1atcLZs2c57ysqKhAXF4cGDRogOjr6d4j4U+Dz+bh79y5UVFSwcOFCBAQEgGEYsqHbu3cvDAwMEBQURJ4VFBTA0dEREydO/J2i/xDYNzMZGRlwdnYm+RMAgdv+n3/+CWtra0RFReHdu3dwcHDAkCFDJH4HjZg0aRIMDQ1J3iB2KMKECRPQpk0bxMXFiYXd0Qj2vI+Li8O4cePg4+ODpKQkTqWznJwctGnTRuJNMa22Q5QUu3v3rhj5VFxcjFmzZoFhGEyePBl2dnYwMDCQGkLtwoULUFVVFdvUffz4EYsWLYKCggLHLtK+2WPr0tWrV8l/bJSWlsLZ2ZkkgL9//z6uXr1Ktd1gky0+Pj5QUlLCgQMHyGHqxIkTkJOTw6hRo8gBZejQoZxQDKFOSiJufjfKy8uhra2NsLAwsXdnz56FhoYGPDw8yDPa9RAA9u/fj759++Lp06eYPHkyGIbB+/fv8fnzZ/j4+MDLy4sTivvlyxeYmZlRV0VLFKL2+vLly3B1dUXr1q1J2N3bt2+xaNEitG/fHp6envDw8ICmpibHLtI4hkKZbty4AQcHB7GxKC8vJ8n7p0yZQg7HNPaFDfaY5ebm4vjx4zh16hSnTWVlJbp06YLVq1eDz+ejoKAA58+fl5q17MyZM+jTpw86d+5MCpl8/foVM2fORNeuXTFjxgzSl+DgYE6uTeHvQxMJKtSpnJwcODo6kgq47PEQFhKaPXs2Pnz48Fvk/Fns3buXkJlBQUGws7NDSUkJqqqqYGRkJEb4Pnz4EO3atcP58+d/g7R1+E9QRzzV4bsQhvi8ffuWVHdjk08xMTFgGAYdO3aEkZERlRvY2vD48WP069cPAwYM4PQJEJBPCQkJYBhG7BaSVqxevRqKiopo1KgRuUkVIiUlBSYmJujYsSPs7OzQpUsXdOrUifqkkJKwa9cuuLq6wtPTk2wmysrKMH78eHTq1AmtWrWCubk51YmAhWD/7oaGhnB2diZ/s8mniRMnUpfz4nuYPn061NTUEB0djQkTJkBfXx/9+/cn43L37l00atSIs3ng8/lSoYuXLl0iRNO9e/fQqlUr9OjRg2z0qqqqkJiYCAcHBwwfPlxqcirw+XyUlJRg9OjRGDBggJiXZH5+PhYtWgRlZWVOzhZawdalWbNmoX379jA0NISioiKCg4M5h4q+ffti2LBhYp+jccwkEbM9e/aEqqoqDhw4QObYyZMnIScnB0tLS5ibm6N9+/ZSsUYLf/PExERYWlqK5S0BBNW1tLW10aVLl18t3j+GkDAzNDQUK4px+/ZtWFpawsHBAcHBwdi+fTt69OgBY2NjKnVQEo4dO0b+LSSfdHR0CPn0/v17bN26FX369JEquwj87Zns5uZGvP+FdqKsrAwRERGwsbHByJEjxTxpaAPbvs2YMQMdO3aErq4uLC0t4ejoyLEvU6ZMEStyAtA7ZqK28fjx44R8EoanFRYWYtasWejSpQvc3Nzg5uaG5s2bcy4aaN+H+Pr6wsLCgnjCs+367NmzYWFhgYkTJ1Jfqbqqqgqpqalo3rw5jI2NoaCggPv375P3Z86cQZs2bWBhYYHs7GwcOHAAXl5e6NKlC7U6WIfaUUc81eGbSExMREhICEn+Wxv5lJubi6NHj0pNwm020tLSYG1tjdGjR3NcvgHBZiIjI4Pq/rBDALOzs6GsrIzmzZtj4cKFePHiBafthQsXsGnTJowfPx5Lly6VmqTUgMBzZsSIEeRvSeRTRUUFzp07h71790qVLgo3SmfPnoWmpiZ69epF3rE3P7GxsVKz0J45cwbt2rUjuRL279+PRo0aERJX2K/WrVtTlfvte+Dz+Th16hQYhsGyZcuIB5ok8gngVvGTBl0UYt++fTAxMZFYvj0/Px+zZs2SqmSeMTExUFVVJSRnaGgoGIbB1atXyZwKDQ2Fo6Pj7xTzp7F3715OwQVJ5NO9e/cwfvx4zJ49m+oqkZLw6NEj9O/fHx4eHuR2n43jx4/D19eXWg9JIdiE+ujRo1G/fn24u7tzwm8BwVhNnToVhoaG6Nq1K3r16iU15Mzt27fBMAymTJlCnkkin0THShrsonDsXr9+DTU1NQwYMEDsQF9WVobg4GD4+/tTr49CLFu2DCoqKrh48SJqamoQFRUFhmFw4sQJ0mbp0qXQ19enXv9EsW/fPvLv48ePo2/fvhzyqaioCImJiRg+fDiGDRsmNfNMqFsFBQXQ09ODu7u7xPxGkyZNwsiRI6Vmjfbx8QHDMBwvVkBgH/766y/06NEDrVq1goGBAVxdXaVmvOrARR3xVIdvYvr06Wjbti0iIiJIiXAh+SRMOC5q1Gg0At+Lcd6wYQPs7Ozg6uqK/fv3c9rRTGCwNzc3btwgz1auXAlNTU3MmzdPLGE1QP9tvijKysqwePFiaGlpYdKkSeS5kHzy8vKS6HIrDX1jo7KyEnv27IGWlhasrKxw584diWFoNPZLdKOdkZGBzp07AxCUQJeXlyf54IqLi5GdnY2KigrMmzePyrn1PcyYMQOqqqpYsWIFyT9w7949aGtrw8XFhZMkGKD/9lQSEhMToaOjg0mTJuHSpUucd58+fZKaZJ58Ph8DBw4k+peeng4lJSXEx8cDAPFOWLt2LSHTaO8TICBlWrZsiSFDhnAqgrHJJ2HYD9tmSNt8u3TpEpydneHq6op169Zx3rH7Rethny0Xn8/Hrl27sHnzZrRp0waDBg0iYZ/s+VRdXY38/HyqEwFLwrZt29CkSRNOEvHLly/Dzc0Nbdq04XgyAPTbDjaEunblyhUoKCigV69euHHjBqcPFRUVZLxp1UchKisrMXz4cFLZOCsri1PpWHhpkpqaip49e0rVWN25cwcMwxAPVkAy+SS6l5KWeSbUrUuXLkFTUxNOTk548uSJmPzC/tE4duz5UVBQgPXr1yMmJgatW7fGwIEDyTv2GL18+RIvXrygupJiHb6NOuKpDgS1LZLz589Hhw4dMG/ePA75JGSeRW/DaQO7X0L5Jb07dOgQJkyYAFlZWcycOZOqiiqSwF5IZs+ejTZt2nBCApcsWQJNTU1ERkYSzycfHx9SRpZmSNLFT58+Yc2aNdDV1UVgYCB5vnv3bnh4eKBr165iYUG04UcW/+rqaty/fx+2trYwNzdHYGAgLly4IBXhMQAIQXHgwAG4ubkhIyMD8vLy5JAPCCo4BQQEcCpK0kimAd9ODjtr1iwoKipixYoVxPPp/v37aNiwIYcgpRW1/eaipLy9vT3s7OywYcOGb7alFYWFhdDR0cHBgwdx7tw5yMnJERKqoqICISEhuHLlCj5+/ChWbYwmSJJp3759MDY2hp+fH3JycshzHx8fqKurY/fu3dRvzn/kgJ6Xl4eAgADo6+vDz88PL168QHFx8S+Q7j8Du2+LFy9GQEAAyeN3+vRp6OrqYtCgQZxy4aI5hGjUxW8hNTUVMjIynAqYV65cgbm5uVQkpRbVR1HiEBB4d7Vp0wYuLi6Ijo7+5mdogage8fl8WFtbY926dTh06BDk5OTIOl1dXY2lS5ciMzMT1dXVUkOmCVFWVobU1FQoKChg5MiR5Pnx48fRr18/mJubc3KpAXTOsx/ZF928eROdOnWCqakpVq9ezcmjCdA5ZmyZEhMTkZiYiBcvXoDP5yM1NRWtWrXCoEGDOJ85efIkJ30Gjf2qw/dRRzzVQQx5eXliLsQRERHo0KED5s6dSzxoXr16BR8fH2oPjKKYMWMG+vXrx6koA3AXm6qqKhw9ehRDhw6FjY0N5syZ890y9r8b8+bNg6qqKk6ePCkWWrd06VK0bt0aPj4+sLGxQcuWLaWGwADAucUHBPnGVq1aBV1dXc7BfuvWrQgKCqJ6IWL3ZeHCheRW8VtYv349AgMDoaWlhbVr11KV5FKI9PR0BAUFAfg7KWRpaSkKCwuhqakJhmE4fS0rK4OHhwcGDx5M5UavNixZsgRbtmwRy90xc+ZMyMrKYsWKFWTD9/z5c+rtovDwC0DMCwHgbuouXryIhQsXQklJCX369EFISAineiRN+Ouvv8g4REREIC8vD4CgCpC1tTUaNWpEqkcCgrBBR0dHrFq1ijyjXS9F1+f9+/ejY8eO8PPz45R7t7W1haen5y+W7p8jJiYG27dvr/V9fn4+zpw5AwsLCzg7O6Nbt264ffs29XMNAKZNmwYNDQ0sX74cDx48IM+F+Uv69euHlJQUeHt7Q1VVVSqq6AJAVFQUFi9eLPY8NTUVDRo0wKxZs8izu3fvUr1GA9zQ6JMnT0psw674PHv2bNjb26Ndu3aIioriVNulCXl5eeRyJDQ0lMyz0NBQ9OjRA82aNeNUqH779i08PT05VZ+lQR/ZKC8vx/bt29G0aVMO+XTixAk4Ojpi+PDhv0+4n0RCQsJ3K+AGBwfDx8cHWlpaWLp0Ka5fv/6LpPvnCAkJgaqqKjZu3Ej2t6Wlpdi+fTu0tbXRu3dvvHz5Eq6urujVq5fU6WAdxFFHPNWBg4MHD0JJSQlxcXGcgwkgIG6aNm2K8PBwUiJXCBo3fmwDdfr0aZiZmYmFi9T2mYqKCpSXl4vlX6ANb968QdeuXcUSr7LJpeTkZAQFBWHcuHFSldPp5MmTUFNT45RuBwSVtSIiIiAnJ4fZs2eLfY7Gje2rV68gKysLPz8/TJ06FXJyct/coIrOp3v37uHt27f/bTF/Gnw+H5s3bwbDMLC2toa8vDwJ+QQElVdatGiBXr16Yffu3UhLS4OLiws6deoksdw0zejbty9kZWWRnp4uRj716tULWlpaWLhwIae8MY12EQAOHz6M8ePHo6ioCBMmTEDr1q05By4hRMfm1atXSElJwfz580moAk24fv06DA0NER4ejnHjxoFhGJKLMDMzE8bGxnB0dCSedvn5+fDw8ICtrS21YyWKVatWwd/fX2wN3rdvH9TV1TFgwAAO+USjPRSCLVtycjI0NDRw+fLlH/rs7du3sW3bNqmoanTkyBFoamqKVc8V9v/8+fPEw9XR0VFqCn5UVVUhPDwcDMNgzZo15Dmfz0dNTQ1Gjx4NhmHEKufSqpPp6ekYNGgQKisrERQUBFVV1VqrggnthZAgXLduHZKTk2slq34nnjx5AoZhEBoaioCAAMjLyxO7eOXKFbRs2RJdunTB3bt3AQjyWHl6esLa2lpq7CIgyIHJ9rID/iafZGRkMGHCBPL8ypUr1OohwJ0jq1evBsMwtRJJ7DEqLi7G1q1bkZCQwFkHaMTOnTuhqakpMQqjpKQEe/bsgba2Nlq3bg1LS0upsYt1+DbqiKf/cUiawKNGjUL79u2RkJDAOUQVFBSgZcuWUFVVxfr162v9PG1ITk7GuHHj4O/vD4DeTc8/wZ07d9C4cWMxl2EAnNxAbCJKGkgnQFBxMDQ0FB07dsSiRYs4765fvw5VVVUwDIOYmJjfJOHPIScnBw0bNoS8vDzZ4EnTpo6NgIAA4kkCAI6OjmAYhpP8XXj4uHjxIkxNTdG2bVtYWlpi4MCB1CeFrM1GjBgxAnJyckhLS+OQTxMnToS+vj48PDykwibGxsbCyMgI5ubmUFFRkejxJApaxwoASWAPCLyc1NXV0bhxY06CXECwgbe0tIS2tjY56Jubm1Ovj2zEx8ejefPmCAkJESOfYmJioKCgAF9fXw4BTPuad+HCBQQFBZH8Td+aQ9IwRqJISkpC165dJVbMEvbn7du31OcukaRHJSUlWLJkCRiG4XgNAoK56ObmJlYljVacP38eDMPAyMgIioqKZA5Jg02XhEuXLhHblp2dDRkZGTRu3BinTp0C8Pd4nj59Gurq6jAzM0Pbtm1hY2MDCwsLqbKLZWVlWLBgAZo2bYqIiAjOu/LycowaNQoMw4iFb9Gul6dOnUJcXBwyMzO/2U5SGCXtiIyMhKurK8rLy2vNF/n161ecPn2aartYh59DHfH0Pwy2wRWdzH/++SfatGmDhIQE4vl0//59BAYGYtWqVVKxEAkxaNAgMAwDc3NzQqRJg1EWhSSZX716BWNjY6xatYpsEoTjmpaWJjWkjOjiL+zr06dPERYWhvbt23PIp0ePHsHPzw9ZWVlU6yK7X5cuXULDhg3RtGlTDkFTW99pRVlZGQwNDaGnp4ebN28CEOQuiYiIgKysLCeprNCuVFRU4MOHD/jw4QP1yXLZ43HhwgXk5eVxQhz9/PwgJyeHnTt3Etf3QYMGIS8vj/pk2+y++fr6gmEYDBkypNZbfUmgrW8xMTEwNDREeno6AGDPnj1o0aIF2rdvj/DwcLEk7xcvXkRiYiLCw8ORkpIiNcUj2NiwYQM0NTUxZcoUUikMANasWQMnJyeMGDGC+gMVINClvLw8yMrKQkZGBsuWLfvdIv1XkJCQAF1dXU5OOz6fj6qqKmzdupWEQAlB49iJht4eOXKE5Fvh8/lYtGgRGIbBypUrUVJSgtLSUvTr1w+7du0in6PNdgghTOYOAAMHDgTDMPD29uZcvEob5syZAx0dHWRkZKCsrAw5OTlo1KgR8XwSrl3CMbl79y527dqFhQsXYs+ePVTbRUDyHHn//j2WL18OBQUFzJs3j/MuOjoa3t7e8PDwoHJ+SUJubi7q1asHWVlZQjxJi+zfglC3/Pz84ODgIPa8uroax48fF8vJS/Nevw4/jjriqQ5YunQpvLy8MGnSJA6rPnr0aHTo0AGTJ09Geno6PD09a600QAtq29gI3aaXLVsmlZsJ9mJTUlLCSR44dOhQ6Onp4dixY+RZeXk5evbsiUGDBlG72ZOEpUuXYtSoURg6dCjJV/XixQvMnDkTenp6GD9+PE6cOAFXV1dOjiAadZGNzMxMvH37Fu/evcOpU6egqKiIP/7443eL9dMoLCwEIMiD4ejoiNatW3M8K1JSUiAjI4OpU6dyPicafiANOhkSEgINDQ00btwYvr6+SElJIe9GjhyJFi1awNzcHCYmJujQoQMn9IJ2lJWVISoqCmFhYbCwsEBgYCAeP34MQDrkZ+PYsWPo168fHB0dceDAAZSXl5NwXFNTU4SFhX03TJVG+8GeI5mZmUhNTeXMo7Vr10JTUxNBQUE4e/YsysrK0Lt3b06OJGkZyy1btkBZWRne3t7EG1QaUdvvferUKaipqWHFihWcnJFlZWXo3r27VBFu06ZNg5qaGhQUFNCxY0dkZWUR78/ly5ejfv366NSpE9q1awdjY2OpCqkuLy/Hli1bsGnTJsjKymLo0KESqwID9M+tgoIC9OjRA1ZWVpxKzfv37wfDMJgyZQrev3//ze+g0S4C4pdDx44dIx7+X79+RWxsLBQVFQn5VFhYiIEDB5JIDdHvoBVv375FbGwslJSUODlNpUF2NmqTNysrCzIyMpy9FQC8e/cO/fr1w4EDB36FeHX4xagjnv7HsXTpUqipqWH8+PGwsbGBqakpVqxYQd7PnTsXVlZW0NXVhZOTE9Uxtmzj9vTpU7x48YJz2z1q1Ci0bdsWiYmJYvmraAa7X1FRUXB2dkaLFi0wcuRInD59GoAg1KlDhw4YOnQoQkNDYWtrK3V5dKKjo6Gqqorhw4fD2NgYqqqqxCX89evXiI+Ph7q6OgwMDNC9e3eqdVEIPp+P3NxcyMrKktvumpoaHDx4EIqKipzkluPHj8fWrVt/k6TfR+/evTFhwgSSnL+oqAj29vYc8qm6uhpbtmxBo0aNEBgYiCdPnsDT0xO+vr5UjxPA1aNLly7BxMQEubm52L9/P/r37w9bW1skJiaSNuvWrUNkZCRmzZpF5hmtG3U2oqOjMWPGDPL3ihUrYGpqSsZLCHYoJY1YuXIlGbOzZ8+iT58+6NatGzIyMkibWbNmwdTUFHPmzCE3/CNGjCCeetKA6dOnQ1VVFS1btkTnzp05FT2Tk5NhamqKFi1aoG3bttTbfPZaJjpX1q9fj5YtWyI4OJiQoNIEdt82b96MBQsWYOzYsSQvS0REBBQUFDB37lwcPXoU58+fh6urK8zMzKj1KgG4enTlyhWYmZnh1KlTePLkCXr27AlDQ0Ns2bIFZWVlAIBz584hMjISsbGxUmUXV61ahVGjRpG/z549CxkZGQwdOpTjqfa9kKffjbVr1xKdKywshIODA8zMzDgEYVpaGhiGQUhICCHlBwwYQH3fRDF9+nQoKiqiRYsW0NTUxKFDhwAI+r169WrIysqiTZs20NfXh5GRkdTYRjY+ffqEmJgYyMjIIDw8/LvtaQNbzn379mHNmjVISkoi4f1jxoxB69atkZCQgI8fP+LWrVvw8vKCubm5VNiNOvw86oin/zGIGqvZs2fj8OHDAAShdFOnTkX79u2xfPly0ubNmzd4/vw51TG27IVk9uzZMDU1hZKSEmxtbTnGeuTIkWjXrh3Wrl0rVhmIdsyZMwfq6urYtGkTcnNz0apVK3Tt2pV4cEVGRqJv375wd3fHpEmTqN/0ierizJkziddWaWkpBg4cCCUlJc4t/5cvX3Dv3j2p0EXh/4uKiqCnp4dz585x2gjJJwsLC9jY2KBt27ZU9keIhIQEMAyDmTNncsgnBwcHMfJp165daNKkCTp06ABTU1PqSUJRXbxy5Qr+/PNP8vedO3cwbNgw2NjYICkpSeJ30Dx2QtTU1MDf3x9jx47lPF+xYgXMzc3h7++P06dPw9XVFebm5r9Jyu8jOzsbGhoaGDZsGHkmJJ/s7e1J2B0gWA/Mzc3h5uYGe3t7qKmpUT1WQl3k8/l48+YNnJ2dcfPmTTx//hxLlixB586dOYR1bm4uMjIysGHDBk6oAm1gz7H4+HgMGzYMfn5+nOIRCQkJ0NDQQHBwMIcElSZMmzYNLVq0wNixY+Hs7AxNTU3ExsYCEFwcWVlZoUGDBjA1NUWPHj2ozqMjmo7h0aNHmDZtGqdN//79CfkkqUABzXONjT///BOurq4A/u73uXPn0KhRIwwePBgHDx5Ez549YWBgQO06dujQIWhqamLChAm4ffs2gL/JJ0tLS2RkZJDwyLS0NDRs2BCenp4wNzdHu3btqK94zP7d8/LyYGpqipMnT+L+/fv4448/SP5FIW7evImIiAisWrWK6v2wKGk9b948jBkzBpcuXUJJSQkqKipI/j52/ipa9VASpk2bBl1dXdjZ2cHHxwcNGzZEXl4enjx5grlz56Jx48Zo0aIF9PX1YWtrS7VdrMN/hjri6X8IbOOWnZ2NU6dOwdXVlZOA9dGjR5g6dSo6dOgglihS9DtoxIIFC6CsrIy9e/dix44diIiIQJMmTTB58mTSxt/fH/Ly8sjKyvp9gv4E+Hw+Hj16BBMTExw5cgQASLz+hg0bxNpLQyJxth6dPn0a+/fvR+/evTnkTFVVFQYNGgRlZWXi+VTbd9AI9ia8TZs2EufTnTt3MHr0aISGhlK9MRJucFJSUsAwDMLCwr5JPgGC/GMnT56kmiQURXR0NJydneHq6ooBAwZw3t25cwfDhw9H9+7dOSWmaYakObJ8+XIYGRmhuLiYHEIAARlgY2MDHR0d2Nract7RhqKiIsTHx8PMzAx+fn7k+dmzZ9G3b18x8ik+Ph6TJk3CqFGjqJ5n7PHKz8/H3bt34eXlRS4XhDf5JiYmnPLgbNDYLzZCQ0OhqqqKyZMno2fPnmjXrh26du1K3iclJUFbWxujR4/meJpIAzIzM9GqVSvicXLixAkwDIOdO3eSNp8/f8bNmzfx8OFDqbGN8+fPR48ePaClpQV3d3cxHRswYACMjIyQmJgoVvGTRkiyi6mpqbC2tkZZWRlqamrImOTk5EBHRwempqawtram/hIlISEB5ubmGDdu3HfJp+zsbEyaNAlTp06lvuIxe8wqKytx//59sTxOw4cPh5ycHNLT01FaWir2HbT2TYjg4GCoqqqiZ8+e6Ny5M9TV1TFv3jx8+PABpaWlWLp0KVRUVMQq99GOLVu2oEWLFqSq+KZNm8AwDFJTU0mbhw8f4uDBgzh//rzU2MU6/DPUEU//I2AvksHBwWjatCk0NTUhKysrZrwfP36MkJAQKCoqchJD0o7CwkK4uroiLi6OPCspKUFKSgqaNWuGtWvXkueLFi2ieoMuujF6/PgxTExMAAAZGRmQk5NDQkICAEH51PT0dE7eJ4DejREb06ZNQ5MmTdC+fXswDINly5ZxNgzV1dX4448/wDAMrl279vsE/UnExsaiQ4cO8PPzw8SJE+Hl5YXQ0FA8ffr0m5+jcaEV1cXk5ORaySddXV2JYUy0zjV235YtWwYFBQVMmjQJVlZWaNSoEZYuXcppf/fuXfTs2RNjx46VivklRGZmJnJycpCfn49Vq1bByspKoq49fPgQ165do3rjJ5StpKQEcXFx6Ny5c63kU23rF439YmP27Nlo06YNunbtik6dOnHeFRUVYc2aNTAzM0Pv3r1/k4T/DHl5eWjdujWpwsrn83H+/HkYGBhwksyuXLlS6sJzAUGYk6+vLwBg27ZtaNasGeLj4wEIcu7cv39fzJ7SeIHC7teGDRvQrFkzLFiwAN27d0fLli0RFRUl5t3k5OQkdXkLN23ahBMnTuDmzZvYsWMHmjdvLjHM8/Xr17h79y7VdpEt05o1a2BqavpD5BP7goHGfgFcfYyMjISbmxvU1dXh7u7OyZkGCEKpFRUVkZKSQvXliSgOHToEDQ0Nzj53/vz5MDIyIkWC3r9/j/DwcLi6ulJvG4G/x23evHkICgoCAOzevRtycnLkPFZYWMgp3iIErXvGOvznqCOe/gfANlAvXryAhYUF8vLycPHiRcyePRuNGzcWO2Ddv38fq1evlqrJ/+XLF2hqamLOnDmc50VFRejfvz8CAgLE+kN7/4Rx0M+ePYOGhgYhBIWbWQC4evUqXFxckJOT87vE/GGIejrZ2trixIkTuHfvHkaOHAk5OTns2rWLc2taVVWFefPmUT9WbGzcuBEJCQkYP348nJ2doaWlBYZhYGxsjG7duqF///4ICwsjN+PSgBs3bpDb3o0bN0okn3r06IHGjRtLRY4Wti6ePXsWcXFxJEfEs2fPEBISgg4dOnDCjoXv2OFQtOPmzZtQVlZG27ZtoaqqCktLSzAMg5EjRyI1NRU5OTl49uwZSc4qBI3zTVKp5TVr1kgkn4QJx7dt2/arxfxpsHVx27ZtUFdXR2JiIiZOnAhlZWX07NmT076oqAiLFi3C8OHDqSQuhBCdJwcPHoSysjIn92JVVRWys7PRoUMH4tHL/gzN/RNFeHg4fH19cfHiRcjLy3MuwZKTkxEWFiYxHI1WHD16FMHBwZy8aePGjYOlpSUWL16M4uJiTntpGqsrV66gc+fO0NbWRpMmTWBra0uq2SUmJiIjIwOfP3/G8+fPOZ+jsY+S1qFVq1ZJJJ8cHR1hbW2N1NRUKm28KNi/97p166CoqIi5c+fCzc0NcnJyYgn7AcDHxwfOzs6/WtSfguhvv2PHDujr6+Pt27ecd2FhYVBXVyderwUFBVRXz5Uk07Rp0xAYGIjMzEzOpTmfz8fGjRsxd+5ciR5qdfh3oo54+hdDmMNIaLgXLlyIPn36wN/fn9xsvH//HgsWLECzZs3EyCchaFycbt68STavU6dOxYULFwAAgYGB8Pb2JgutEOPGjYOXl9cvl/M/wbFjx8AwDCGfIiIi0LhxY05+lvLycnh7e8PT05PKDZEQol4w69evR0BAACZMmMB5Pnz4cMjLyyM9PV2iyz6NN3LfSpgrfDd//nxoa2sjJycHkZGRGDx4MPr370/l3JKEnTt3wsDAAJs2bSJjIIl8KiwsRGBgINX9mjp1KkmECwiq4jAMg2bNmhFPDAB48uQJIZ8khdbROt8kyVVYWIjCwkIcP34ce/fuRb169aClpQVLS0s0bdoUSkpKCAgI+A3S/jNER0cTkrA28uncuXNwdHQUszE0Y9euXdi4cSOp8lNaWor09HTo6OiIeTeVlpZSTc4IbQIgIDAAAWmrq6uLzZs3c9q+ffsWampq2LRpE+c5jQcrUSxYsAChoaEABB6DLVu2BMMwnD6WlZXBy8sLY8aMobZPISEhnIuQ06dPw8jICM2bNyd5QAFBmJOQfFqyZIkYkUajLgLicgnHIT8/H48ePUJWVhaaN2+OHj16wMDAAM2bN4eamhr69u37O8T9R1i/fj0ngqE28qlTp04YPXr0b5LynyEnJwfjxo3jpMiYMGEC2rRpg9WrV4vlbKVVD0WRm5uL6upqbNu2DWpqasRuComYr1+/QklJCXv37uV8jlY7IsSmTZtIoark5GR06NABcnJyWL16NWnz9etXeHh4ICws7HeJWYffgDri6V+KiIgIkigREByIly1bhkaNGsHCwoLT9v3794iKioKSkpJY2B1t4PP5uHv3LlRUVLBw4UIEBASAYRj89ddfAIC9e/fCwMAAQUFB5FlBQQEcHR0xceLE3yn6T+Pjx4/o0aMHoqOjAQC3bt3CyJEjoaCggJCQEISEhMDJyQkdO3Yknig0Lrbx8fGkipaQkBg2bBgYhoG1tbXYhoHtKk17skv27x0XF4dx48bBx8cHSUlJnNDHnJwctGnTRsyrRPQ7aMWnT5/g7u6O7t27IyUlRYx8mjVrllhpZlrJJxsbG0JaAIJD7+LFi9GsWTPMmjWL0/bJkycIDQ2FoqIiJ08LrWDr0tWrV8l/bJSWlsLZ2Zn05/79+7h69SqVpK4kFBUVoV+/fmAYhhQeYJNP7ITjf/31F7Xza/jw4bh8+TL5++XLl2jWrBkYhuF42ZWWlmLXrl3Q1dWVeAim8QCyf/9+9O3bF0+fPsXkyZPBMAzev3+Pz58/w8fHB15eXhyS98uXLzAzM5OKOSaKlStXwsDAAHfv3kVVVRVWrlwJHR0dTJkyBc+ePcOpU6fg4eEBY2Njaqtqff78GV5eXrh48SJ5VlpaisjISGhqaqJ///6ctau6uhqBgYFo3bo11ZVYhWDbgNzcXBw/flwsb2RlZSW6dOmC1atXg8/no6CgAOfPn6d2HRNFQUEBRo8eDSMjIyxZsoQ8F5JP48ePJ+RTSUkJ1f2KiorC1atXyTw5ceIE2rRpAzU1NbHKe0LyKS4uDp8+feK8o9H27927l9jxoKAg2NnZoaSkBFVVVTAyMoK9vT2n/cOHD9GuXTucP3/+N0j7z1BWVgYPDw+Op27//v3RrFkzpKWl4fHjx7h9+zbc3d1hbm4uNXuPOvzfoI54+pfixo0b6NOnDyc5Z0lJCdavX48GDRogMjKS0/7Dhw+YMWMGXFxcqNsUScLq1auhqKiIRo0akdtUIVJSUmBiYoKOHTvCzs4OXbp0QadOnahOClnbAhkUFARjY2Py94MHD7B69WqYm5ujb9++CA4Opj4pZFpaGjQ0NPDy5UvO8xkzZkBdXV1qXaXZmD59OtTU1BAdHY0JEyZAX18f/fv3JzkG7t69i0aNGnE2D3w+nzpd/PjxY60yffnyBV5eXrCxseGQT8JEkYmJib9S1H+EmpoaDBw4kFNsABDcekdHR6NJkyaE6BXi4cOHiIuLo3qjDnDt2qxZs9C+fXsYGhpCUVERwcHBnDwKffv2JQQN+3M09lEoE1vOV69e4c8//0SDBg0IgfH161fExcXB3Nwc3t7enO+g8QAyYsQIzJ8/n/zN5/Nx5swZdOrUCXZ2dpyxKCsrw+7du9G4cWOpuB0+e/YsNDQ0YGhoCGVlZdy6dYu8u337NiwtLeHg4IDg4GBs374dPXr0gLGxMZX69z1cvXoVxsbGxEvt5cuXWLNmDVq1agVlZWWYmJjAy8uL+ipN/v7+YofeiooKLFiwAF26dMGUKVM44TBVVVVYunQptf0Rgm03ZsyYgY4dO0JXVxeWlpZwdHTk2IYpU6Zg1KhRYt9Bex+FePr0KaZOnQpTU1MsXryYPF+9ejUsLCwwZMgQTp5JGvv17t07dO/eHWfOnOE8j4yMhLq6Ovz8/PDu3TvOu4kTJ6Jp06bYvXv3rxT1p1FVVYXU1FQ0b94cxsbGUFBQIBENAHDmzBm0adMGFhYWyM7OxoEDB+Dl5YUuXbpQOVaSIJxv169fh5ycHCfU3dPTE506dULDhg1hZWUFOzs76u1iHf7vUUc8/Uvx8uVL6OrqioWIVFRUIC4uDvXq1UNUVBTn3ZcvX6iOHa6pqSFyZWdnQ1lZGc2bN8fChQvx4sULTtsLFy5g06ZNGD9+PJYuXUo9OSPEixcvOCFmxcXFaNWqlZgnhqgnEM1Gu7i4GH379kVycjIAruyBgYHQ09OTalfpM2fOoF27dsjNzQUguO1v1KgRCbUQ6mzr1q05uTJog5GREedQu2XLFhw8eJDT5vPnz/D09ISxsTG2b99O5tOBAweon1tCXLhwAaqqqmKu6/n5+Vi0aBEUFBTEyCchaJ5nQsTExEBVVZWQnKGhoWAYBlevXiXyh4aGwtHR8XeK+V2EhITgwYMH5O8PHz4A+Hs+vX79GiNHjkTDhg1JZdaCggIsXrwYI0aMoNZ+COWPjY1Fjx49xN6dPXsWmpqacHd357wrLS3FyZMnqdZBNpk+evRo1K9fH+7u7rh37x6n3b179zB16lQYGhqia9eu6NWrF/UHEPaeSDRp8ZQpU6ClpUUuUPh8PkpLS3H58mU8f/6c6qTUwn7duHEDDg4OYl5nZWVlCA8PR9euXcXIJyFoHTM2li1bBhUVFVy8eBE1NTWIiooCwzCcqs5Lly6Fvr4+1f2ZNWsWJwG1aBqDZ8+eISgoCKamphzPp+joaKrtIhthYWFwdHQkeY2ECA8PR+fOnTF79mwxD+vY2Fiqx40NHx8fMAwDhsG64gAA2BRJREFUDw8PzvOqqir89ddf6NGjB1q1agUDAwO4urpSbRslnRX5fD7Kysrw559/YtSoUSgoKCDvbt68iYMHD+LGjRtU28U6/PdQRzz9CyE0BMnJyWjVqhXOnj3LeS8knxo0aCDxgEUr6SSEsGR7TU0NVq5cCU1NTcybN0/Mowag/zafjY0bN0JXVxd+fn64f/8+kXf+/Plwd3fHu3fvwOfzUVNTIxWbBzYmTZoEQ0NDsgCxc+xIm6u0qEwZGRno3LkzACA9PR3y8vKcioPZ2dmoqKjAvHnzqF1gIyIiYGxsTPpWWVkJfX192NnZcUJiAMEBWEdHB9bW1khISODMK1r7JwSfz0dJSQlGjx6NAQMGiOWCE5JPysrKmDlz5m+S8p+Dz+dj4MCBRP/S09OhpKREihEISe21a9eSyjg02nsXFxfY2toSfcrMzISMjAwJnxbK/OrVK/Tv3x+ysrKkuEJxcTHVuY+EKC8vh7a2tkQPJqHHkOjBRAga1zL2b83n87Fr1y5s3rwZbdq0waBBg0jIJ/tyq7q6Gvn5+eQZ7fYDEBAYM2fO5Hivvnr1Cl27diVhWpJCxGnWReDvCyI3NzeSUFs4LmVlZYiIiICNjQ1GjhwpMf8izaisrMTw4cNJJa2srCxOpWNhnqrU1FT07NmTSpsIAG5ubujYsSORLysrC4aGhsTbTognT55g6NChaN26NSfBPe12UShfTk4OHB0dceDAAQBcezdr1iyYmppi9uzZ5DKCDdptY0FBAdavX4+YmBi0bt0aAwcOJO/Ysr98+RIvXryQGnJm1apViIuL45BMW7Zsgby8vFi4Pxu06mId/nuoI57+xXj8+DH69euHAQMGcFzdAQH5lJCQIJYEk0awNwHCMtNsmZcsWQJNTU1ERkYSzycfHx9cuXLll8v6n6CiogLLly9Hr169ICcnh0mTJuHkyZN48eIF5OTkpDL/BXvsDA0NOeFzbPJJWlyl2bh06RIAgbePm5sbMjIyIC8vz6k4ePDgQQQEBHBCXmncGE2dOhVmZmYAgODgYKSmpuLVq1fo0qULnJyccOzYMU57X19fqKmpYeLEidRu0r+Fffv2wcTEBMHBwXjy5AnnXX5+PmbNmiU1JYvZKCwshI6ODg4ePIhz585xKshUVFQgJCQEV65cwcePH6mtzHf//n0YGhoST4ScnBwcP34cPXv2hLa2NufiARCUZ2YYBgzDkDkJ0NcvNoQ2IDExEZaWltixY4dYm3PnzkFbWxtdunT51eL9NNiHh8WLFyMgIIAcQE6fPg1dXV0MGjSI46khup7RPF5shIaGws3NDQoKCpg+fTrJFTRu3DgxDzZpAduLUE1NDQMGDBDzQC4rK0NwcDD8/f2pPyyK6hKfz4e1tTXWrVuHQ4cOQU5OjqzT1dXVWLp0KTIzM1FdXU36RlsfHz58CDMzM0J4XrhwAXl5eRgwYADs7e2xZcsWTvtTp06hWbNmaN68OWe/LC3zzNfXFxYWFkQP2WTu7NmzYWFhgYkTJ4rpKW1g61FiYiISExPx4sUL8Pl8pKamolWrVhg0aBDnMydPnuR4VdKmi6IoKSlBUFAQZGVl4e3tjdmzZ5N3w4cPh4eHh8T8pnX430Qd8fQvR1paGqytrTF69GhyWyxEWVkZMjIyqGfShZg3bx5UVVUJGcPG0qVL0bp1a/j4+MDGxgYtW7akPjE1G6JjkJycTJLxCUkBQ0NDvH379jdJ+M8hXDSFISS9evUi79ibINpdpdPT0xEUFATg76SQpaWlKCwshKamJhiGITeowN8JFgcPHkztZk8o19mzZ2FgYABjY2M0a9aMhMY8f/4cZmZmcHJyIqXO+Xw+Ro8ejdOnT1NLXvwIEhMToaOjg0mTJnEIC0CQTJ3msGNAkDhbmMA+IiICeXl5AARhCtbW1mjUqBEJbwUEhJqjoyNWrVpFntHYtwcPHqBdu3aIiorCsGHD0LlzZ3z58gU3btxAr169oKGhwVnLcnJyMGbMGKxatUpq1jIhHj16hP79+8PDw4Pc7rNx/Phx+Pr6Un/wEGLatGnQ0NDA8uXLOWGSwtwl/fr1Q0pKCry9vaGqqsoJn5cmfPr0Cenp6TA3N4eJiQlGjBiBI0eOoEGDBli/fv3vFu8fQbj2XrlyBQoKCujVqxdu3LghFmJIKzEjRF5eHvGcDg0Nxfbt28m/e/TogWbNmhEyHhAUl/D09OSkpaBRJ798+QINDQ2MGDECgYGBkJGRQXV1Nf766y8MHjwYtra2HPLp4sWLGDBgAJKSkqjeV4lCqFcFBQXQ09ODu7u7xLV40qRJGDlyJJVjJQkhISFQVVXFxo0bSb7F0tJSbN++Hdra2ujduzdevnwJV1dX9OrVS2r6xcbDhw8RFhaGDh06oE2bNli2bBlmzZoFHx8fsZDQOvzvoo54+hegthhbITZs2AA7Ozu4urpi//79nHbCBYn2DfubN2/QtWtXsZthNrmUnJyMoKAgjBs3jtqcTpIO6rVtvr9+/Ypz587ByckJKioq6Natm1QuRkJUVlZiz5490NLSgpWVFe7cuSPxFoTGTRKfz8fmzZtJJT55eXnieQEIDr8tWrRAr169sHv3bqSlpcHFxQWdOnWitpKRKNzc3MAwDLy8vDjPnz9/Dmtra1haWsLDwwP29vbo1KkT0WUaxwuoXS5R22hvbw87Ozts2LDhm21pwvXr12FoaIjw8HCMGzcODMMQr9bMzEwYGxvD0dGReNrl5+fDw8MDtra21I4XG9u3b0fTpk3RtGlTTgXC69evo1evXlBTU0N2djYho8aMGUPa0Gbzv4dLly7B2dkZrq6uWLduHecde6xoPegLceTIEWhqaoqF9gvlPn/+PGxtbWFubg5HR0eqi31ImiPC0EA23r59i6NHj6JLly7o2LEjGIahvnquqB6JhkgCggTwbdq0gYuLC6Kjo7/5GZrw5MkTMAyD0NBQBAQEQF5entjFK1euoGXLlujSpQvu3r0LQODh5enpCWtra6rtovD3vnv3Lho2bAh5eXmOR//169cxZMgQWFlZYeHChbhz5w48PT05Hsk0908Uwv5eunQJmpqacHJywpMnT8Rsu6TCEzRi586d0NTUlBiFUVJSgj179kBbWxutW7eGpaUl1bbxe6iqqkJZWRmCgoLg6+sLBQUFMAzDSXZfh/9t1BFPUg72BuDZs2e1vjt06BAmTJgAWVlZzJw5E2lpab9Mxv8L3LlzB40bNxbLNwOAQ16wiShaDyDl5eUYNWoUcnNzOZuBtLQ0TviBcNEpLi7GxYsXSVtaN30/skhWV1fj/v375AASGBiICxcuUOudFhAQQDxJAMDR0REMw2DEiBHkmTDv1sWLF2Fqaoq2bdvC0tISAwcOpDopJBufPn2Ct7c3IiMjYWhoiD/++IPz/u3btwgPD4efnx/Gjh1L5hatusjOM8CuGiMEW+6LFy9i4cKFUFJSQp8+fRASEiIxgS4NECawBwReTurq6mjcuDEnQS4gqGJkaWkJbW1tMtfMzc2p10ehDUlKSgLDMJCTk0N0dDSnIt+dO3cwYsQIMAyDtm3bonPnzlRv1H9kjuTl5SEgIAD6+vrw8/PDixcvUFxc/Auk+79DUlISunbtyll3RQ+9b9++lZq8JSUlJTh8+DDJZyTsC3s9YCMtLQ3h4eFU90mYywgQhPNIgnBs3r9/j9mzZ8Pe3p54IN65c+dXiPnTuHTpErEB2dnZkJGRQePGjUkYpLBPp0+fhrq6OszMzNC2bVvY2NjAwsKCersoxJYtW9CwYUM0adIE/v7+HF27efMmpk2bBkVFRejp6UkFgfEjv/fNmzfRqVMnmJqaYvXq1cTLVwha9yBsREZGwtXVFeXl5bV6Un/9+pXjRU6zHfkW2P168uQJNm7cCG9vb6ntTx3+71FHPP1LMGPGDPTr1w8fP37kPGcbgaqqKhw9ehRDhw6FjY0N5syZI1bGngZIWiRfvXoFY2NjrFq1iiymQgOdlpaGmJiYXyrjfwonJyc4ODiQG5C9e/eiXr16WLNmDaed6KJK68bowoUL5N8LFy7khJzVhvXr1yMwMBBaWlpYu3Yt53BJA8rKymBoaAg9PT3iJrx48WJERERAVlYWwcHBpK1wUa2oqMCHDx/w4cMHqUqYCwh0i8/nY8OGDejQoYMY+SQ6L2nt1+HDhzF+/HgUFRVhwoQJaN26NefAJYRof169eoWUlBTMnz9frJQzDYiJiYGhoSHS09MBAHv27EGLFi3Qvn17hIeH482bN5z2Fy9eRGJiIsLDw5GSkiI13q2A4BLl69evWLduHeTl5SX27/LlyxzynvZ+xcTEkLAfScjPz8eZM2dgYWEBZ2dndOvWDbdv36bW5osiISEBurq6nHx2fD4fVVVV2Lp1q1QUjmAjJSUF8vLy2LlzJyGiMzIywDAMxz5IGh8adTE9PR2DBg1CZWUlgoKCoKqqKjE5MwDOJRefz8e6deuQnJxcK1n1OzFnzhzo6OggIyMDZWVlyMnJQaNGjYjn07t37wD8be/v3r2LXbt2YeHChdizZ4/U2A8AuHbtGl69eoVz585BXl5erEpdcXExnj17hgsXLkgVgZGQkEDGqTYEBwfDx8cHWlpaWLp0Ka5fv/6LpPvnEOqWn58fHBwcxJ5XV1fj+PHjYk4D0mLza0NtRKc06GId/vuoI56kFOyJffr0aZiZmYnlKantMxUVFSgvLxcrcUwD2ItoSUkJ53Zj6NCh0NPT4yQ6Li8vR8+ePTFo0CBqb3XYYPevX79+cHZ2xtKlSyEnJycx1Eca8OrVK8jKysLPzw9Tp06FnJzcN29GRRfVe/fuUZe7qrCwEIDghtjR0RGtW7fmhNalpKRARkYGU6dO5XxOdGMuDTopiuLiYiQnJ6NDhw4YOnQoeS7Jk4FGxMbGwsjICObm5lBRUZHo8SQKadjoHTt2DP369SPVfsrLy/Hx40dERETA1NQUYWFh351H0tBPUaxcuZKQT7X1j8Z+sW19cnIyNDQ0cPny5R/67O3bt7Ft2zZO5TRaUBthdOrUKaipqWHFihWcC62ysjJ0794dy5Yt+1Ui/p8hNjYWbdu2xfHjx7Fz504oKCggKSnpd4v1j3D+/HkwDAMjIyMoKiqS9YxmW/4jKCgoQI8ePWBlZcVJJbF//34wDIMpU6bg/fv33/wOGu3H98bl8OHDaNasGYd8Ev0MreQuW67Vq1eDYZhaiST22BQXF2Pr1q1ISEjAxYsX/+ty/ixq+72zsrIgIyMjVn3w3bt36Nevn8T8fv8mSLuNqcP/LeqIJylHcnIyxo0bB39/fwD0LjQ/ArbsUVFRcHZ2RosWLTBy5EicPn0agCDUSXggDg0Nha2trVTl0QG44YA2NjZo0KCBVJZuZyMnJ4fkHhDmT6BxM/cj6N27NyZMmEC8B4uKimBvb88hn6qrq7FlyxY0atQIgYGBePLkCTw9PeHr6ysVOvg9FBcXY+PGjejYsSM8PT1/tzg/BLb98PX1BcMwGDJkSK23+pJA49itXLmSkwS+T58+6NatGzIyMkgbYYnpOXPmkJvjESNGUJ3Qk31YYpf5lrSGrVy5EgoKCoiMjOR41EgDLly4gKCgIJK/6Vs6RrvNZI/N5s2bsWDBAowdO5YcGiMiIqCgoIC5c+fi6NGjOH/+PFxdXWFmZiZVt93sfq5fvx4tWrRAo0aNxDySpQHs3FQDBw4EwzDw9vbG169ff7Nk/xnWrl1L9K6wsBAODg4wMzNDVlYWCY9MS0sDwzAICQkhpPWAAQOQmZn5u8T+Lti2EACptCfJbhw+fBgKCgoYNWoUlWvX93Dq1CnExcV9dzwkVSmkDWybsW/fPqxZswZJSUnk4mvMmDFo3bo1EhIS8PHjR9y6dQteXl4wNzen3u7XoQ7/l6gjnqQcgwYNAsMwMDc3JxsJGo3yz2DOnDlQV1fHpk2bkJubi1atWqFr166kf5GRkejbty/c3d0xadIksqGVFuMtHJ9Dhw5BXl4e5ubmsLOzw9WrV6Vq7NgL7aVLl9CwYUM0bdqUk/9I9BApDf1LSEgAwzCYOXMmh3xycHAQI5927dqFJk2aoEOHDjA1NaU+p8LPoLi4GGvWrMHgwYOlitAuKytDVFQUwsLCYGFhgcDAQDx+/BiA9BHz2dnZ0NDQwLBhw8gzIflkb29Pwu4AQYlpc3NzuLm5wd7eHmpqatQf9svLyzF8+HDk5ORw7PfOnTvFyoMLb8bZlfpoBp/PR15eHmRlZSEjIyOVHj+1Ydq0aWjRogXGjh0LZ2dnaGpqIjY2FoDg0sjKygoNGjSAqakpevToITU5dNgQzp2TJ09CVlYWzZs3x+7duwmpIW0oLy/Hli1bsGnTJsjKymLo0KF4+fKlxLa028lDhw5BU1MTEyZMwO3btwH8TT5ZWloiIyODlKNPS0tDw4YN4enpCXNzc7Rr147anJJCVFZWwtzcnHjCCOXdv38/p1Q9IEjqzzAMoqKifrmc/wlyc3NRr149yMrKEuKJdr37EUybNg26urqws7ODj48PGjZsiLy8PDx58gRz585F48aN0aJFC+jr68PW1lYqbWMd6vCfoI54kiLUdpgVxusvW7ZMqm+x+Hw+Hj16BBMTE1K6XRivLykMjfZE4pKSCAr/Lep6O2DAANjb23NyJUkLMjMz8fbtW7x79w6nTp2CoqKiWH4gaYFwfFJSUsAwDMLCwr5JPgGCUMOTJ09KVU6FH0VZWZnYDSzNiI6OxowZM8jfK1asgKmpKfFKE6K2BMG0oaioCPHx8TAzM4Ofnx95fvbsWfTt21eMfIqPj8ekSZMwatQoqSHk3d3d0b17d5I4fe/evahfvz5Wr14NgKt36enpUje/tmzZAmVlZXh7exNvUGlGZmYmWrVqRbxNTpw4AYZhOIUxPn/+jJs3b+Lhw4dSbRfT0tIgLy+P7du3Y/ny5Wjbti3S0tJQVlb2u0X7KaxatQqjRo0if589exYyMjIYOnQox4OQZk8gUSQkJMDc3Bzjxo37LvmUnZ2NSZMmYerUqdRWPBbF7NmzISsrS/bC6enpaNq0KTZt2iTW9tKlS9T3RxRv375FbGwslJSUMGnSJPJcGvYZtWHLli1o0aIFSXuyadMmMAyD1NRU0ubhw4c4ePAgzp8/L9W2sQ51+KeoI56kBGxj/PTpU7x48YKTbHXUqFFo27YtEhMTORWdaIfoIvP48WOYmJgAECTylJOTQ0JCAgCBB0Z6erpYVQsavUuEGx4AYomNP3z4gP79+2P9+vWc505OThg3btwvke//Anw+H7m5uZCVlSWb15qaGhw8eBCKiooYPnw4aTt+/Hhs3br1N0n6YxDVxeTk5FrJJ11dXYlhTLQf8v8paJxjoqipqYG/vz/Gjh3Leb5ixQqYm5vD398fp0+fhqurK8zNzX+TlD8OoT6WlJQgLi4OnTt3rpV82rVrl8TvoHlDy55vgwcPhoODA2JiYiTmuxOdmzT2iy2jqB1Yv349WrZsieDgYOJ9Jy0Qnftr166Fr68vAGDbtm1o1qwZ4uPjAQjy7dy/f19svGg8TAqrkdaG69evQ0dHhxNeFxsbCzk5OYnVdWnGn3/+CVdXVwB/j8W5c+fQqFEjDB48GAcPHkTPnj1hYGBAva1nz/01a9bA1NT0h8gn9p6MRvshCdHR0ahfvz7mzZsHBQUFMs9qA639qm2effr0CTExMZCRkUF4ePh329MK4ZyZN28egoKCAAC7d++GnJwcKbRTWFgosYDOv3XPWIc61IY64kkKwN4IzJ49G6amplBSUoKtrS3HWI8cORLt2rXD2rVr8eXLl98g6T+HMA762bNn0NDQQEhICBQVFTkL7dWrV+Hi4oKcnJzfJeZ3sWfPHs7fixcvhoODA/r374+NGzeS5+xbRvbCQ/uCK+rFVVRUBD09PZw7d47TRkg+WVhYwMbGBm3btqV2UySKGzduEG+6jRs3SiSfevTogcaNG0vdIfLfBElzZfny5TAyMkJxcTHnoBEfHw8bGxvo6OjA1taW845GSCq1vGbNGonkkzDh+LZt2361mP8x2F6r3bt3R/369REWFvYbJfpnYOtifHw8hg0bBj8/PyxcuJA8T0hIgIaGBoKDgzned9KG8PBw+Pr64uLFi5CXl0dcXBx5l5ycjLCwMIlVJGlCcXEx5+/09HQsX74cBw8eJNX3Xr9+TdY19vhu376d6sOiJLuYmpoKa2trlJWVoaamhqzFOTk50NHRgampKaytrakPFZck16pVqySST46OjrC2tkZqairV4yUKYc474Tj6+fmhXr16hNCQpr4A4nnh5s2bhzFjxuDSpUsoKSlBRUUFYmJioKCggIiICNKWVh0UQpJ806ZNQ2BgIDIzMzmX5nw+Hxs3bsTcuXNJhcw61OF/FXXEkxRhwYIFUFZWxt69e7Fjxw5ERESgSZMmmDx5Mmnj7+8PeXl5ZGVl/T5BfxLHjh0DwzCEfIqIiEDjxo05ngvl5eXw9vaGp6cnteRMUlIS9PT0SC6P+Ph4KCkpITw8HA4ODujatSuCg4NJ+9oIJ1r7xwb7YNGmTRusWrVKrM2dO3cwevRohIaGSk3Yz86dO2FgYIBNmzYRmSWRT4WFhQgMDKS+P/8LyMzMRE5ODvLz87Fq1SpYWVlJJDkfPnyIa9euSZV7e3R0NA4dOgSgdvLp3LlzcHR0xIQJE36XmD8ESV4wws17dnY25OX/H3vnHRbV9fTxuXSVJgIREAXBggWlCAgWxAKIgooaFLGBCCZiQcSSWIiKImBQ7EqNgoqgRsGS2EDEhlixVwxRESx02P2+f/Du/e0Cpkfumvt5njyRs+fuM2fvvXPmzJkzowIrKyvY2dnh0qVLnF94NEZwcDA0NTUxa9YsDB8+HB06dIC1tTX7+datW9G2bVv4+PhIVZL0FStWIDg4GEDde6SjowOGYRAfH8/2qaiogIuLC3x9fTl97xYsWICxY8eym3Nz586FtrY2jI2N0alTJ3h6erLlzcXHUV/Xc133x8XF4eTJk7hx4waSk5Ohra3d6EbJixcvkJ+fL1V6cceOHVi6dCn798ecT926dYOPj08TSfnHaMzeEz1bqampUFFRwahRoyArK8tWdOb6s9cYgYGB0NTUxPDhw9GzZ0988cUXWLp0KV69eoXy8nKEh4ejVatWmDdvXlOL+qeIi4vD999/DwBsNWBlZWX2qDhQN3c7OztL5aYKD88/De94khLev3+PIUOGSOwulpWVISEhAaqqqmw4JwCsXr1aqiamoqIiODg4IDQ0FABw8+ZNTJkyBWpqapg3bx7mzZuHgQMHomvXruyOHBedM0+ePMGMGTNgY2OD0NBQzJ8/H+np6QDqJp5ly5bBwsJCYmKVpvskIiIiAp07d4aXlxdmzpwJFxcXBAcH4/Hjx795nTQYtG/evGFzziQkJDRwPi1evLhBaWZpvIefCzdu3ICGhgaMjY2hqakJKysrMAyDKVOmYPfu3cjOzsaTJ09QVlYmcZ003LMPHz5g9OjRYBgGp06dAiDpfBJPOH7t2jVO6sTGWLZsGZvPCaiLElVQUGCdGOPHj0ffvn1x7ty5phLxL5Gbm4t27dqxx7CEQiHOnTsHExMT2Nvbs/2ioqKkrvplVFQUTExMkJ+fj5qaGkRFRcHAwABz5szBkydPcPr0aTg7O8PU1JTTFWaFQiG+/fZb2Nraws/PDzk5OXB1dcWVK1dQWVmJ7du3w97eHq6urnj27BkAbtoav8fly5fRs2dPtG3bFs2bN4ednR1bzW7Lli1ITU1FcXExnj59KnGdNIz13bt38PHxQffu3bF27Vq2XeR8mjFjBut8KisrkwpdDwBhYWFISkpi/05JSYGKigq2bNkCoC56XlFREYcPH24qEf8yR48eha6uLq5evcq2fffdd+jevTvCwsIAAC9fvsSyZcswZMgQTuqOxqioqICzszOGDx/Oto0ZMwaqqqrYu3cvHj58iFu3bsHJyQkWFhZSYQPz8Pzb8I4nKaGkpAR6enr49ttvJdo/fPiAMWPGwM/PTyp25D5m2MyePRumpqbs3/fu3cOGDRtgYWEBd3d3BAYGcjoppGhcBQUF8PPzg52dHQwNDSWSGBcXF2P58uWwtLREUFBQU4n6t4mNjcXmzZsxY8YMDBo0CG3atAHDMDA1NUWfPn0wZswYLFy4kE0+y0WKioo+atyUlJTAxcUFtra2Es4nUaJIkSHI8+lpTH+8f/8e79+/x88//4xDhw5BRkYGbdq0gZWVFVq0aIGWLVvCz8+vCaT9c4j0tfhzWVBQAG9vb8jJybEOjbdv32Ljxo2wsLDAsGHDJL6DiwtH8fH88MMPUFNTw5kzZwDUvWtffvllg3x3Tk5ODXJ1cQ3Rby0aX3p6OjQ0NCRyL9bU1CAjIwOdO3dmkwSLX8PF+9UYV65cgampKVsM4/nz54iOjoa+vj40NDTQo0cPuLi4cLpCk/hvvnbtWvTv3x9ubm4YOXKkxNHbhIQE9jOR84nrC+GPVY99/fo1Hjx4gAMHDkBbWxsODg4wMTGBtrY2tLS04O7u3hTi/m0eP36MuXPnwszMDGvWrGHbN2zYAEtLS4wfP15iI4yLz6P4PYuJiYGuri4uXbqE2tpa1NbWwt3dvUFOp2+++QZ9+/YFwO1nsv7vnZycjI4dO6KwsFDis4ULF+KLL75giyK9e/eu0aI8XEQkX15eHpSVlSWOug8dOhTdunWDvLw8bGxs0K9fP07rRh6eTwnveOIgN27cYI3XuXPnspXOvv76awwbNozdzRHh7+8PFxeXTy7n3+HZs2cSZYlLS0uhr6+PxYsXS/SrX/aWi0q7vtH3/PlzzJgxAyoqKg1Ca4uLi/Hdd99BX19fIhSXq/xWwlzRZ9999x3atm2L7OxshISEYNy4cRgzZgwn7xUAdO/eXeK+JCYmspFpIoqLizF06FCYmpoiKSmJdT4dOXKEk47P/wLiz+KVK1fY/8QpLy/HoEGD2Apbd+/exZUrVzh9z+bNm4d79+6xf7969QrA/wzbFy9eYMqUKZCXl8fJkycB1Bnoa9asweTJk6XGeXH8+HHMmjVLItcdAIliEdKS70505BYATpw4AaAu4tXQ0FDi+BlQV71JS0urQTUqLi6sxGWqnwdtzpw5aNOmDYqLi9m+5eXluHTpEp4+fSoVR7VEMgoEAqxevRomJiYwMDBoUKUuMTERDg4OsLOzaxDhyjXE35MLFy7g559/xunTpyX6VFdXo1evXtiwYQOEQiHevXuHc+fOcXaOBoDFixdLRMjUL+bx5MkTzJ49G2ZmZhKRT6GhoVKlF8+fP4/Zs2dj+/btAPC77xEX9cbHuHDhAmpra7Fr1y5oaWmxelOU5+jt27do2bIlDh06JHEdF8fYmExCoRAVFRXw9vbG1KlTJQo73bhxA+np6bh+/bpU6EYenk8F73jiEEKhEPn5+WjVqhVWrVoFPz8/MAyDa9euAagrM21iYoLZs2ezbe/evcOAAQMwc+bMphT9TxEbGwtDQ0N4eXnh7t27rPHz3XffwcnJCb/++iubXJHrxoO4fImJiWxUQmFhIWbMmAFLS0tERERIXFNUVISYmBhOG32A5Ng2btwIf39/uLq6YuvWrRKLxezsbBgZGTU4zlT/O7jA8uXLYWpqyspVXV2Njh07ol+/fg0qFZWXl8PAwAC9e/fG5s2bJe4Xb0B8WsSNvsWLF6NTp07o0qUL1NXVERgYKFEtxt3dnT2G9ls5WrjA4MGDYWdnxz5PaWlpUFBQYPW7SP6CggKMGTMGioqKbHGF0tJSqYmcycrKgqmpKTQ0NJCcnAzg4+8Q1/PdHT58GO7u7nj8+DFmzZoFhmHw8uVLFBcXw9XVFS4uLhK6pKSkBObm5qwzVBqIjIzEokWLJI47FhQUwNramnVe1N8UArh5v4DG5RIIBIiMjESHDh0wdepUCWciAGzZsgVfffUVZ8cESOq3BQsWoGvXrjA0NISVlRUGDBggIfucOXMwderUBt/BRb3o6OiIrl27suM7cOAAunTpwkbciXj06BEmTJiAdu3aSaShkAa9KBQKkZubC0VFRSgoKLC5QYHfl5uLjhmgbo0iiqKbPXs2+vXrh7KyMtTU1KB79+7o37+/RP/79++jQ4cOUnWsev369di4caOEkykxMREqKioNNsLE4fKzyMPzKeEdTxxkw4YNUFdXh5KSErubKiIhIQE9evRA165d0a9fP/Tq1QvdunXjfDUScaqqqrBu3TqMGDECysrKCAgIwKlTp/Ds2TMoKytLlYEuIigoCDo6OoiKimIN2OfPn8Pf3x/W1tYNnE8iuGj01Wf+/PnQ0tJCaGgovvrqK3Ts2BFjxoxhd8Tz8/OhpKQkYTyIKrNwjblz58Lc3BxAXbLL3bt3o6CgAL169cLAgQPZ5J0i3NzcoKWlhZkzZ3JyPP81wsLCoKmpyT5rwcHBYBgGV65cYd+l4OBgDBgwoCnF/EPcvXsXXbp0YaOYsrOz8fPPP2P48OFo27Ytrl+/DuB/Buv+/fvBMAwYhsHFixfZ75GG57KmpgahoaHQ19fHoEGD2MTO0miMZ2ZmQldXF126dIGGhgZu3rzJfnbr1i1YWVnB3t4egYGBSEpKgoODA0xNTaVC14sIDg6Go6Mj1NTUMH/+fDaCxt/fHw4ODk0s3Z+jfkTQtWvX2GPgQqEQ4eHhsLGxga+vLxvN9VvfwUUiIyPRqlUr5OTkQCAQYOXKlWAYhtUtABAeHo6OHTty/jm8f/8+zM3NWR1//vx55ObmYuzYsejfvz8SExMl+p8+fRqqqqrQ1taWiDaUBr0I1DktNDQ0MGzYMOTn5ze1OH+Zmpoa7N69G9ra2jA1NYWamhpbMAgAzp49CyMjI1haWiIjIwNHjhyBi4sLevXqxflnUkRZWRlmz54NRUVFDBs2DN988w372aRJk+Ds7NzoBiwPD8//4B1PHKF+hR8NDQ1oa2tj1apVbJ4BEefPn0dcXBxmzJiB8PBwTuc+qk99GWNiYthkfCKnQJcuXVBYWNhEEv55tmzZAm1tbeTm5rLOGNG9FB27s7W1lSgVKy2cPXsWHTp0YJMBHz58GEpKSqyBJxpnu3btkJqa2mRy/h4iOTMzM2FiYgJTU1Ooqqrizp07AICnT5/C3NwcAwcOZHOxCIVC+Pj44MyZMw3yufB8eoRCIb788ku2RPG+ffvQsmVLNg+G6Ojutm3b2ASlXL5f9+7dQ4cOHbBy5UpMnDgRPXv2RElJCa5fv44RI0ZAV1eXjXwC6hxTvr6+WL9+Pad1fWPV64A6J3t4eDhbfUqU14Pri3oR4s+Tj48PZGVl4eTkxOoQEXfu3MHcuXPRpUsXWFtbY8SIEVKZ3+PNmzfYt28fLCws0KNHD0yePBnHjx+HnJxcg5xc0kBgYCC0tbWho6MDDQ0NBAYGoqKiAkKhEGFhYbC1tYW/v3+DyCcu6xCgLmp30qRJbIGZAwcOSBScEVWg3b17N4YPH8758ZSUlEBXVxeTJ0/G119/DQUFBdTW1uLatWsYN24c7OzsJJxPOTk5GDt2LLZu3crp9+u30hbs2LEDOjo6CAwMbLTyoDTh6uoKhmHg7Ows0V5TU4Nr167BwcEB+vr6MDExwZAhQ6RSN96/fx8LFy5E586dYWRkhMjISCxevBiurq4NjoTy8PBIwjueOID4hCS+yx0VFQU9PT0sXboUz58/b3Adl4+RNLZQF3euifP27VtkZWVh4MCBaNWqFfr06cNp46i+bD4+PvD39wfwv/sgfk9/+eUXjBs3DtOmTeP0uICGi8DU1FT07NkTQN1CX0VFhV34l5aWIiMjA1VVVVi6dCmnF8PiODo6gmGYBnnRnj59it69e8PKygrOzs7o378/unXrJrFw5mk63r9/DwMDA6SnpyMrKwvKysrss1hVVYV58+bh8uXLKCoqkhpHYVJSElq0aIEWLVrg6NGjbHteXh5GjBgBLS0tZGRksM4oX19ftg8X3zdx/bF9+3b4+fnBx8eHdVQLBAKsWbMGNjY2mDFjBut84vp9Eh+XUChESkoK4uPjYWRkBA8PD/aIhXhi3NraWrx+/Zpt49r9akyfieQWp7CwECdOnECvXr3QtWtXMAwjFUf7xZ+prKwstGvXDmfPnsX58+exe/duNGvWDJ6engDq7m9YWBiMjY3ZKltcpf67IhQK0bt3b2zfvh1Hjx6FsrIy64wXOXvT0tJQW1srkeOKi4jkys/Ph7y8PFRUVHD58mX287y8PIwfPx42NjZYtWoVbt++jaFDh0pEJHNxnhb/vTdt2oSJEyfCy8sLq1atYts3b94MXV1dBAYG4tGjR00h5l9CfGzv3r3Djh07EBYWhnbt2uHLL79kPxO/L8+fP8ezZ8+kOvdRTU0NKioqMHv2bLi5uUFNTQ0Mw0gku+fh4WkI73hqYsSNiG+++QZGRkYS4cJr166Fnp4eQkJC2MgnV1dXicmYq1RWVmLq1KlsgkERe/fulThOJ/oNSktLkZOT06jzhiuI3y9R0vdevXphypQpDfpUVVWxEQuvXr2SmsUwAPYoz5EjR+Do6IjU1FSoqKhIVFlJT0+Hn58fXrx4wbZx0egT582bNxg2bBhCQkLQpUsXduEhorCwEMuWLYOXlxemT5/OGkRcfBY/Z65du8bmEVu+fDlbHXLhwoXo3bs3lJSUEBMTw/Z//fo1BgwYgPXr17NtXH7PRLJt3boVDMNAWVkZoaGhErmqbt++jcmTJ4NhGBgbG6Nnz55Sc6Q6KCgI2tra8PDwgJubGxiGgbe3N8rLy1FbW4vQ0FDY2dlh/PjxKC0tbWpxfxPxd3/NmjXw8/Nj83ucOXMGhoaG8PDwkEiEXP+4OFfvV1lZGY4dO8ZGC4rkFK/GKs7evXuxbNkyqVooxsTEwNvbG4GBgRLt586dg6ysLOtoEggE2LVrF6fnsNzcXLx58wZA3XHIpKQk9t8ODg5QVVVlnfFA3Xw2dOhQREVFsW1cfRbFSUxMhLy8PJo3b45p06ZJPG83btxAUFAQ1NXV0b59e1hZWUmNXgwODoampiZmzZqF4cOHo0OHDrC2tmY/37p1K9q2bQsfHx8Ju4qriOvGLVu2YMuWLXj27BmEQiF2794NfX19eHh4SFxz6tQpicIF0mpbiT9rjx49QmxsLIYNGyZVupGHpyngHU8cYenSpdDU1GRzHYkTHh6Odu3awdXVFba2ttDR0Wk0sScXGThwIOzt7VlHmajceXR0tES/+pMPF42/+gmOTUxM8Pr1a6xcuRJdu3ZFVlaWRP87d+7Aw8ODjWIDuDvJ7tu3D7Nnzwbwv6SQ5eXleP/+PfT09MAwDBu6DwAVFRVwdnbGuHHjOG/s1ae2thZCoRA7d+5E586dGzif6o+HNyQ+LXl5eejSpQuWLVsGf39/MAzD5tFJS0uDqakpBgwYwBrmr1+/hrOzM+zs7DipN36LJ0+e4O3bt9i+fTtUVFSwbNkytqKpiEuXLkk477n+PGZlZUFHR0ci59tPP/2E5s2bIyAgAEDdGBYtWgRfX1/O6sT6BAUFQVdXF+vWrZOoQijKXTJ69GgkJCRg2LBh0NTU/GiEL5dISEiAiooK9uzZw1aaSk1NBcMwOHv2LNuvsfeK688hUBdZ4erqChUVFUyePBlAnX4XLXwXLVoEGxubBrmduKhHHj16BIZhEBwcDD8/P6ioqLB68fLly9DR0UGvXr3YPEEvXrzA0KFD0bt3b06O57e4evUqCgoKkJWVxd47cT1RWlqKJ0+e4Pz581ITNZObm4t27dqxhQeEQiHOnTsHExMT2Nvbs/2ioqLg5ubGed0hzrx586CpqYnY2Fh286S8vBxJSUlo27YtRo4ciefPn2PIkCEYMWKEVI3tt/jYOLj+LPLwNCW844kD/PLLL7C2tmar/YgQdy7FxMRg9uzZ8Pf3l4qcTuJGwujRozFo0CCEh4dDWVkZO3fubELJ/j6XLl3CsGHD2IXV2bNnYWtrCw8PD5w6dQrA/wzePn36cN7oEwqFiI+PB8Mw6N27N1RUVCScZdnZ2WjdujVGjBiB/fv3Y+/evRg8eDC6devGPoPSaEiUlpYiJiYGnTt3xoQJE9h28fdKGsclrYjyiAF1UU5ffPEFmjVrJpEgF6grvmBlZYW2bdvCzs4OFhYWsLCwkMpcEeJERUWxzqeP5bjj4tjqy5SRkQEjIyMUFxdLHN1KS0uDoqIi69AQd8xw3fl0/Phx6OnpITMzU6JdJPe5c+fYZ3HAgAFSE4EBABERETA2NsbPP/+MPXv2QE1NDVu3bm1qsf4xMjMzMXr0aDRr1kziOCsArF69GtbW1pzeyLt48SIrX0ZGBhQUFNCsWTM24bvoGTxz5gy++OILmJubw9jYGLa2trC0tOS8Xvy9d+TYsWNQVVWVcD7Vv4aL+qO+rOnp6dDQ0JDYWKipqUFGRgY6d+7M5pYUv4aL46rPnj17oKen1+gpjLKyMhw8eBBt27ZFu3btpCo67a/wOY6Jh+efhnc8cYDbt2+jWbNmDcq5A5CokCBuHHHZ6SRCXF5bW1vIyclh0aJFTSjR3yc+Ph5Dhw6Fg4MDm7QTqEvoOXToUGhqasLIyAhdunSBubk5+xtw0YDw8/OTOFIxYMAAMAzD7gwDdROpQCBATk4OzMzMYGxsDCsrK3z55ZecN2j/CKWlpYiNjUXXrl0xdOjQphbnP0tYWBi6dOmCffv2AQAOHjyI1q1bo1OnTo1GAeXk5GDLli1YtmwZEhISOB0NJL4AEV9QNKYToqKioKamhpCQEKk4aiHK0QQAV65cYXWFjIyMhIMJAJ49e4Z27drh4MGDEt8hDcb61q1bYW1t3ahTWvTsFRYWSlXeEvHnb8eOHWjdujWUlJQaRCNLC+Ljqf9uZWdnY/To0TA2NkZ6ejqqq6tRUlICBwcHTkeXfPvttzAwMEBqaioqKiqQnZ0NJSUlNvLp119/BfC/ZzE/Px8pKSlYtWoVDh48yGm9WN+5IspB1di9OHbsGNTU1DB16lTO3itxxBPUiypTP3nyBIaGhhKpNIA6vaGlpYW4uDiJdmkYJwCEhIRgyJAhqKyslMhxJ87bt28lirRw8Xnk4eH5NPCOp09MY5NJQUEBTE1NsX79+gaOir1793I+2eXHEI316NGjUFFRgYWFBfr164crV65IzaRan+joaBgYGEBDQ4NNKCvi4cOHOHXqFCIjI5GSksJpo6+iogJdunRB+/bt2Soca9aswfLly6GoqCiRD0Mkf1VVFV69eoVXr15xNmHuX6G0tBTR0dEYN24cJx2E/wV++uknjB49GgMGDMCRI0dQWVmJoqIiLF++HGZmZli4cOHvVrrksgO0srISkyZNQnZ2toSce/bsaVAefMOGDWAYRiKHFRc5efIk3Nzc8ObNGwQEBKB9+/YoKipCaWkpxo4dC3t7e4kotjdv3qBz5844cOBAE0r919i8eTMMDQ0lnIFCoRA1NTX44Ycf2Lw7IqRFj4j096lTp6CoqAhtbW3s37+fzfkkLYj/3tu2bcPEiRPh4+ODjRs3su2ZmZkYMWIEGIZBx44dMXnyZFhbWzfIb8Ul3r17BwcHB9jY2ODw4cNs++HDh8EwDObMmYOXL1/+5ndwWS9WV1fDwsICR44cYf8G6sYnXqoeqIs6ZBgGK1eu/ORy/hkOHz4Md3d3PH78GLNmzQLDMHj58iWKi4vh6uoKFxcXiU3mkpISmJubN8gLx3VEz5WXl5fEUUFRe21tLX7++Wc8efKk0et4eHj+m/COp0+IuHFUVlbGJs8FgAkTJqB9+/b46aef2LbKykoMHz4cHh4enDSKRDS2yyH694EDB6CgoICEhAQAwNixY9G/f382MTeX+dhvvnv3bnTq1Amenp5sjoWP9efiJPv+/XsAdWWWBwwYgHbt2kkcrUtISICCggLmzp0rcZ3oGKEILj+TfxZRWW1AehaNnwNRUVHs756ZmYlRo0ahT58+SE1NZfssXrwYZmZm+Pbbb9kd/smTJ0td2WInJyf07duXdcYcOnQIsrKy2LBhAwDJ527fvn2cd+rGxsaiX79+MDExgYaGhkQZ8KNHj2L48OHo3r07tm3bhj179sDR0RFmZmac1IkiPvbunz59GlpaWvj+++8l8gFVVFSgb9++iIyM/FQi/uPs3bsXKioqSEpKwrp162BsbIy9e/eioqKiqUX708yfPx86OjqYOXMmZsyYgbZt2yIoKIj9/Pz58/Dw8ED79u0lchaKJzvmAtu2bUNeXh6Auvna3t4e5ubmOHDgAOso27t3LxiGwbx581in/NixY5GWltZUYv8lvvnmGygqKrJHzfbt24cWLVo0iAAC6o4dcl0vZmZmQldXF126dIGGhoaEjXjr1i1YWVnB3t4egYGBSEpKgoODA0xNTTmtF4GP68b6Nr6IX3/9FaNHj2adijw8PDwA73j6ZIgr7ZUrV2LQoEFo3bo1pkyZgjNnzgCoO+okyjcTHBwMOzs7zufRETfYxI+eAXWV3MaMGYMdO3ZItA8cOBD+/v6fRL6/ivj9evHiBR49eiSxC7x9+3aYm5tj2rRpuHXrVlOI+JcYOXIkvvrqKzYU/MOHD+jfv7+E86m2thaJiYlQUlLC119/jUePHmHo0KGcPpLwT/G5j49LZGRkQFdXFxMnTmTbRM6n/v37s8fugLrFiYWFBRwdHdG/f39oaWlxfgEiQlyXjBs3Dvb29ggLC2s03119456LYxR/R7y8vMAwDAYPHixRkQ+oy303a9YsqKqqolevXnBxceH08Vzx3z4+Ph4rVqzA9OnTWQfA8uXLoaamhiVLluDEiRM4d+4chgwZAnNzc07eJ+B/R6U/Rl5eHgwMDCSO10VEREBZWbnRo/9cJi4uDsbGxsjJyQEAJCcnQ0lJCc2aNYOvry/b7/Tp0/D09ET37t3ZPElc4ujRo9DT08NXX33F2hYi55OVlRVSU1NZu2vv3r2Ql5fH0KFDYWFhgQ4dOnA6X9XHCA0NhaysLJYuXQo1NTWJ6rmNwcX3TfwotY+PD2RlZeHk5IQ7d+5I9Ltz5w7mzp2LLl26wNraGiNGjOC0XgQkdeOPP/6I6OhobN26FXfv3gUA+Pr6ol27dti8eTOKiopw8+ZNuLi4wMLCgrNj4uHhaRp4x9Mn5ttvv8UXX3yBuLg4XLhwAfr6+rC2tmZzZYSEhMDd3R1OTk4ICAhgJ1iuKe/6eTrWrFkDe3t7jBkzBrGxsWy7+NEE8TFwOapEXLYlS5bA2toazZo1g5eXl8T5/K1bt8LCwkJiccJ1Nm/eDIZhsGjRIgnnk729fQPnU0pKCpo3b47OnTvDzMzss04KyfPp+fDhAzZt2gRzc3N4eXmx7ZmZmXB3d2/gfNq0aRMCAgIwdepUzurFjyG+GOzbty9kZWWxcOHCJpTor1G/stTmzZsRERGBgQMHwt3dvcEiC6jLd/L+/XupOZ4bFBSE1q1bY/r06Rg0aBD09PQQEREBoG7TyMbGBnJycjAzM4ODgwNnF42lpaUSf+/btw/r1q1Deno6ezTwxYsXbDVW8XublJTEufHUp74NsWHDBoSEhACoiyZUV1dHZGQkIiMjwTCMRORTdnY2PD090aZNG3bjj0ts3rwZFhYW8Pf3/13nU0ZGBgICAjB37lypKDwjQuSoEd1HLy8vyMjIsNV1uf78iSP+LAqFQqSkpCA+Ph5GRkbw8PBg0zKInw6ora3F69evpUYvAnW60dDQEP369YOrqyvk5eWRm5uLR48eYcmSJWjWrBlat26Njh07ws7OjrO6kYeHp+ngHU+fCKFQiAcPHqBHjx5sSLEoUWRjVd64nEh869ataN++PXu8YNOmTWjZsiWWLVsGe3t7WFtbS+QI+pjDicvOJ6DO6aSlpYXU1FTk5OTA3t4e3bt3Z4/GAHWRT23atMHq1aubUNI/hsjASUhIAMMwWLhw4W86n4C6/GOnTp3ik0Ly/KOInqeysjJs3LgRPXv2/KjzKSUlpdHv4OqzWF+viSfMzcjIgIqKCqysrGBnZ4dLly5JjSNXfFzh4eFYt24de8QnLi4O/fv3h7u7O7sLDtTlgSovL2/0O7hIWloa9PX12Y2EkydPgmEYifwrxcXFuHHjBu7fv89ZvbhgwQKMHTsWJSUlAIC5c+dCW1sbxsbG7DFxUe4V8eev/gJRGhaMoaGhbE7Fhw8forCwEKampmxuzGvXrqFVq1ZgGAYrVqxgrzt79iy8vb0ljog2NeLPUXR0NMzMzP6Q80k88pxrz6KIxt590fOVmpoKFRUVjBo1CrKysmzKCWl4/sTHtWbNGvj5+eHdu3cA6ioNGhoawsPDA1evXmX71c/nJA1zQGJiIlq3bo2LFy8CqNP5DMNg9+7dbJ/79+8jPT0d586d46xu5OHhaVp4x9O/SP2J9uHDh+jRoweAuolWWVkZmzdvBlC3O7lv3z6JvE8ANyekJ0+eYMaMGbCxsUFoaCjmz5+P9PR0AHXVK5YtWwYLCwvMmzePvUYaDAhxMjMz0b17d7Yy05kzZ6CkpIQ+ffrA1NQUW7ZsYfuKV4/hKvWfxZiYmI86nwwNDRvNn8P1MfJIB41VvImOjm7U+SRKOL5r165PLebfZtmyZRLJtQ8ePAgFBQU2anL8+PHo27cvzp0711Qi/iVEEUHr16+XOF4XHx8PBwcHuLi44MSJE3B0dISVlRUn5zAR9WXbtm0b3NzcAAC7du2Cqqoqe+zn3bt3uHv3bqOORS4hFArx7bffwtbWFn5+fsjJyYGrqyuuXLmCyspKbN++Hfb29nB1dcWzZ88AcG8Mv4W4rHFxcdDV1WWP1wF1c3WHDh3Ysd26dQuenp7IyMhoMIdxKY9VY+/J+vXrG3U+DRgwAL1798bu3bulbl4OCwtDUlIS+3dKSgpUVFRYm2rNmjVQVFSUSKYuDQQFBUFXVxfr1q3DvXv32PazZ8/CyMgIo0ePRkJCAoYNGwZNTc2PVvDjGiIZly5dykaj7d+/H8rKymyetPfv3zc4ag3wNiMPD09DeMfTJ0C0A/zkyRPo6upi3rx5UFdXlzjHfuXKFQwePBjZ2dlNJeYfQmT0FRQUwM/PD3Z2djA0NERubi7bp7i4GMuXL4elpaVEeLs0UVhYiHXr1qG6uhrHjx9Hq1atEBMTg19//RVGRkbo1KlTg+oq0jDJXr9+nY2mi42NbdT55ODggGbNmnFqJ5jn8yM0NBRHjx4F8HHnU1ZWFgYMGICvvvqqqcT8w4gvIn744Qeoqamxx3hKSkrw5ZdfNsh35+TkhOnTp39SOf8OBw4cgK6uLi5fvsy2iTsCkpOT4ejoCH19ffTv31/q8s0sW7YMbm5uyMnJgYqKikRVtJiYGCxcuLBBLkMuIV4gYe3atejfvz/c3NwwcuRIiaiYhIQE9jORg0YaFsHiZGdnY/bs2di6dSuA/z2H169fR8uWLfHdd9/h8ePHcHZ2xtixY9nx1dbWcnqsO3bswNKlS9m/P+Z86tatG3x8fJpIyj+OuH6IiYmBrq4uLl26hNraWtTW1sLd3b1BTqdvvvkGffv2BSAdz+Xx48ehp6eHzMxMiXbR2M+dOwc7OztYWFhgwIABnE9b0JhcQUFB+Prrr5GWliaxaS4UChEbG4slS5ZIRLfy8PDwNAbvePqX+emnn8AwDOt8Wr58OZo1ayax2KisrMSwYcMwdOhQTu881pft+fPnmDFjBlRUVBrkKykuLsZ3330HfX19iaNpXORjv/mHDx9QXV2NUaNGYfHixaxjaeTIkejWrRsCAgI4azg0xp49e2BiYoK4uDg2/Lkx59P79+/x9ddfS4UjjUc6+fDhA0aPHg2GYdhqieLOJ/GE49euXeO0XqzP8ePHMWvWLIlcdwAkolmlJd9dfaKjo9mF08eKXrx+/Rr5+flSc9RixYoVCA4OBlB3VERHRwcMw0jk86uoqICLiwt8fX05r/NFv7tAIMDq1athYmICAwODBtE9iYmJcHBwgJ2dHV6+fNkUov4lBAIB8vLyoKSkBDk5OaxZs4b9TCgU4v379wgJCYG6ujoMDAxgYWHB+YW+iHfv3sHHxwfdu3fH2rVr2XaR82nGjBms86msrEyq5ujz589j9uzZ2L59OwD8rn7g+r0SZ+vWrbC2tpYYi7ijE6jbzHz27JnU6EWgLqLw+++/B1DnNOzcuTOUlZUlbPq3b9/C2dlZKnMW8vDwfHp4x9O/TFFRERwcHBAaGgoAuHnzJqZMmQI1NTXMmzcP8+bNw8CBA9G1a1fWOOLiQkRcpsTERLbiTWFhIWbMmAFLS0s2AauIoqIixMTEcNo4Eh9XTk4ODh8+jLy8PNYJU11dDXNzcyxYsABAXS6FcePGITk5WSJRpDTw5s0btqR7QkJCA+fT4sWLGyxAuHzveKQH0XMk/q4UFBTA29sbcnJyrD55+/YtNm7cCAsLCwwbNkziO7ioF+uTlZUFU1NTaGhoIDk5GcDHFxjSlO9OxMKFC6Gvr8/+LbqvtbW1OHXqVAP9IQ3jioqKgomJCfLz81FTU4OoqCgYGBhgzpw5ePLkCU6fPg1nZ2eYmppyusJsY7+1QCBAZGQkOnTogKlTp7LzmogtW7bgq6++4vx9auz3Tk5OhpaWFpycnBpUln3//j3u37+PU6dOsc+oNCz0AeDx48eYO3cuzMzMJJxqGzZsgKWlJcaPH4/Hjx+z7Vyfo4VCIXJzc6GoqAgFBQU2Nyjw+/qBi+9ZY2zevBmGhoYSxXSEQiFqamrwww8/sMn8RXD9fQPqnO3Ozs4YPnw42zZmzBioqqpi7969ePjwIW7dugUnJydYWFhIzfvFw8PTtPCOp3+Qj00ms2fPhqmpKfv3vXv3sGHDBlhYWMDd3R2BgYFSU40kKCgIOjo6iIqKYo3Y58+fw9/fH9bW1g2cTyK4aByJGzULFiyAoaEhunXrBhMTE7i5uSE7OxtVVVWYNGkSHBwcMHv2bAwcOBBmZmYSu8pco6io6KMGW0lJCVxcXGBrayvhfBIlihTPXcXD83eZN2+eRL6LV69eAfjfu/fixQtMmTIF8vLyOHnyJIC6Xf81a9Zg8uTJnHy/fouamhqEhoZCX18fgwYNYpM7S9s4PibvuXPnYGRkhNWrV6OyspJtLy4uhoODg0SiWWnhypUrMDU1RUJCAoC6+Sw6Ohr6+vrQ0NBAjx494OLiwukKTeL368KFC7h27RqbIF0oFCI8PBw2Njbw9fVFcXHx734HlxCfy5KSktgIDKDuSKuuri4CAgJw//79Rq8BuHfPFi9eLJFsun5OxSdPnmD27NkwMzOTiHwKDQ2VSr0I1G1YamhoYNiwYcjPz29qcf4SH/vdT58+DS0tLXz//fcS71dFRQX69u0r4WyTBkTvT15eHpSVlSVyLA4dOhTdunWDvLw8bGxs0K9fP07rRh4eHm7BO57+BZ49eyZhlJeWlkJfXx+LFy+W6Fc//wXXlfaWLVugra2N3NxcNl+EaIISHbuztbXF8uXLm1LMP83GjRvRunVr9nz+/PnzoaKiwlYfvHr1KqZMmYJ+/frB3d2d05Fp3bt3lwh5TkxMZBO/iyguLsbQoUNhamqKpKQk1vl05MgRzjs+eaSHwYMHw87Ojn2m0tLSoKCggGvXrgH4n+4oKCjAmDFjoKioyOa4Ky0tlchXw0U+lmS6trYW4eHhbF6Wt2/fNtqfq4jLuW/fPqxevRqRkZHsvZkxYwbs7Owwb948PHv2DNnZ2XBxcYGlpSWn9Ye4M0I83xEAzJkzB23atGEXjUKhEOXl5bh06RKePn0qNcdjAgMDoa2tDR0dHWhoaCAwMBAVFRUQCoUICwuDra0t/P39G0Q+cTWyRPxZvHnzJszMzGBpack6CYG6TRM9PT3MmjVLwvnEVRwdHdG1a1f2Nz9w4AC6dOkiMSYAePToESZMmIB27dpJ5BqTJr1Y36bdsWMHdHR0EBgYKHU5JMXHFR8fjxUrVmD69Omsg3f58uVQU1PDkiVLcOLECZw7dw5DhgyBubk55/VGY++/UChERUUFvL29MXXqVLZSH1DnKE1PT8f169elRjfy8PBwA97x9A8TGxsLQ0NDeHl54e7du+zE+91338HJyQm//vorhEIhBAIBZw0HEfUnIx8fH/j7+wP4n0EhPoZffvkF48aNw7Rp0zhryDbGxIkT2WSeBw4cgKqqKhv5U1ZWhurqajYRpmhcXJxkly9fDlNTU/aeVFdXo2PHjujXrx97lElEeXk5DAwM0Lt3b2zevFnCQOTi2Hiki7t376JLly5sFFN2djZ+/vlnDB8+HG3btsX169cB/E9/7N+/HwzDgGEYtlwzIB0L4u3bt8PPzw8+Pj5sXiCBQIA1a9bAxsYGM2bMYJ1PXB1PY4iqNI0bNw6DBg1C+/btERsbi9LSUixcuBCmpqaQkZFB165dJRKJc30DJTIyEosWLZKoJlhQUABra2ts2LABQqGw0aToXJyvxZ+nrKwstGvXDmfPnsX58+exe/duNGvWDJ6engDq5A8LC4OxsTHCwsKaSuS/xLx58+Du7g5bW1toaGigU6dOEon64+Li0LZtW0yaNAnPnz9vQkl/m/v378Pc3Jx99s6fP4/c3FyMHTsW/fv3R2JiokT/06dPQ1VVFdra2hI5x7iqR8TfkU2bNmHixInw8vLCqlWr2PbNmzdDV1cXgYGBePToUVOI+bcQVfWcPn06Bg0aBD09PTbSf+XKlbCxsYGcnBzMzMzg4OAgNXoRqMsltnHjRgknU2JiIlRUVHDlypWPXsdF3cjDw8NNeMfTP0xVVRXWrVuHESNGQFlZGQEBATh16hSePXsGZWVl7Nmzp6lF/EOIGzbnz58HAPTq1QtTpkxp0KeqqoqNYnj16hU7CXHROKovk0AgwOjRo3HkyBGcOXMGysrKrNOppqYGW7ZsQWpqaqNJI7nG3LlzYW5uDqBu53v37t0oKChAr169MHDgQPz0008S/d3c3KClpYWZM2dydkw80sm9e/fQoUMHrFy5EhMnTkTPnj1RUlKC69evY8SIEdDV1WV1BlDnmPL19cX69eulyvEZFBQEbW1teHh4wM3NDQzDwNvbG+Xl5aitrUVoaCjs7Owwfvx4lJaWNrW4f5iUlBTo6+uzZep37twJJSUl/PDDDwDqnNrV1dU4deqUVCUSB4Dg4GA4OjpCTU0N8+fPx+nTpwEA/v7+cHBwaGLp/hoxMTHw9vZGYGCgRPu5c+cgKyvLOpoEAgF27dolFYtgEbGxsVBXV8eVK1dQXFyMwsJCDBkyBL1790ZMTAzbb9OmTXBzc+P0IrikpAS6urqYPHkyvv76aygoKKC2thbXrl3DuHHjYGdnJ+F8ysnJwdixY7F161apumfBwcHQ1NTErFmzMHz4cHTo0AHW1tbs51u3bkXbtm3h4+MjkReJ66SlpUFfX5+Ncjp58iQYhpGw64uLi3Hjxg3cv39fqvRiWVkZZs+eDUVFRQwbNgzffPMN+9mkSZPg7OyMsrKyJpSQh4fnc4B3PP2D1J9cYmJi2GR8IqdAly5dUFhY2EQS/jHEnRCLFy+GiYkJXr9+jZUrV6Jr167IysqS6H/nzh14eHiwUQwAN3dAxGUSzzsTEBAAVVVVNG/enF1YAXW5kgYMGCCRY4GLiO5XZmYmTExMYGpqClVVVdy5cwcA8PTpU5ibm2PgwIHs8UGhUAgfHx+cOXOG045CHuklKSkJLVq0QIsWLXD06FG2PS8vDyNGjICWlhYyMjJYZ5Svry/bRxoM9aysLOjo6EhEzvz0009o3rw5AgICANSNY9GiRfD19eWkTqyPSAeEhoZi5MiRAOqcUCoqKqxD/v3798jNzW1wrTSMT8SbN2+wb98+WFhYoEePHpg8eTKOHz8OOTk5iUgaaeD58+dwdXWFiooKJk+eDKDuPoqOEy5atAg2NjYNcjtJiyNj8eLF6NOnj0SUuChCzdjYWKJ6ZGOR2FxBJFN+fj7k5eWhoqKCy5cvs5/n5eVh/PjxsLGxwapVq3D79m0MHTpUYmNIGu5Zbm4u2rVrx0ZZC4VCnDt3DiYmJrC3t2f7RUVFwc3NjdN2R33Ztm3bBjc3NwDArl27oKqqik2bNgGoy0149+7djx7Blhbu37+PhQsXonPnzjAyMkJkZCQWL14MV1fXBrnIeHh4eP4svOPpL9DYQl0gEDQ6gb59+xZZWVkYOHAgWrVqhT59+nB6ohXn0qVLGDZsGLuwOnv2LGxtbeHh4cGWQBcZvX369OG0USQ++YeEhGDw4MHIyMgAULcIGTZsGFq3bo2SkhK8e/cOhYWFcHJygo2NjVQsgkU4OjqCYRi4uLhItD99+hS9e/eGlZUVnJ2d0b9/f3Tr1k0iLw0Pzz+BSL9t3boVDMNAWVkZoaGhKCgoYPvcvn0bkydPBsMwMDY2Rs+ePTlf8rz+O5KRkQEjIyMUFxdDKBSyn6elpUFRURFnz54FIDk3cHERIv57f/jwAQAQHh6OwMBAHD9+HMrKyti8eTPbNzk5GStWrJA4jsE1GtNn4vdIRGFhIU6cOIFevXqha9euYBgGM2fO/FRi/mNkZmZi9OjRaNasmYSTFwBWr14Na2vrRo8PchnRcxkSEgJLS0tUVFQA+F9uzJMnT6J58+YYOHAgkpKSGlzHVRITEyEvL4/mzZtj2rRpEvbFjRs3EBQUBHV1dbRv3x5WVlac14v17eH09HRoaGjgl19+YfvU1NQgIyMDnTt3Zje/xK/hol5sjGXLlsHNzQ05OTlQUVGRyL0VExODhQsXsjpUmqmpqUFFRQVmz54NNzc3qKmpgWEYiSqLPDw8PH8F3vH0F6msrMTUqVNx4cIFCWN27969EmG3oom1tLQUOTk5nN6REyc+Ph5Dhw6Fg4ODxER64MABDB06FJqamjAyMkKXLl1gbm7O6YTb4syfPx+ampo4cuQInj17BqBO5uzsbFhYWEBdXR0mJibo1auXhNEnDY4ZkQMtJCQEXbp0YXN7iCgsLMSyZcvg5eWF6dOnswYv1+8Zj3Ty5MkTvH37Ftu3b4eKigqWLVsmsRgB6pzb4jqUq05eUY4moK4KmkAgQE5ODmRkZCQcTEBdcYl27drh4MGDEt/B1YWjiO+//x5bt24FABw6dIjNuSWeW6a0tBSDBw9mI7q4TFlZGY4dO8YW+hD9/o1FawF1c/eyZcs4+wwCkrq6vt7Ozs7G6NGjYWxsjPT0dFRXV6OkpAQODg6cjyz5La5fvw5ZWVksW7ZMov3o0aNwd3eHg4MDBg0a1CBhPFe5evUqCgoKkJWVxUapid/L0tJSPHnyBOfPn+f8US3xJPUnTpwAUKf3DQ0NJfQGUGd/aGlpIS4uTqKd68/lihUrEBwcDKAuGkhHR6eBXqyoqICLiwt8fX05P54/gvgYHj16hNjYWAwbNoyzzyEPD4/0wDue/gYDBw6Evb09Gy596NAhyMjIIDo6WqJffQNRGpwY0dHRMDAwgIaGRoOkgg8fPsSpU6cQGRmJlJQUzi8aRZw5cwZGRkZs8uLKykoUFhYiIyMDZWVlqK2tRUxMDHbs2IG0tDSpGZc4ogToO3fuROfOnRs4n+obRdI0Nh7pJSoqinU+feyoMVf14smTJ+Hm5oY3b94gICAA7du3R1FREUpLSzF27FjY29vjwoULbP83b96gc+fOOHDgQBNK/eeZOnUq2rZtyzpqwsLCICsri5iYGOTl5SE3NxdDhgxBz549Wb3B5UVWQkICVFRUsGfPHpSXlwMAUlNTwTAM6ywEGn/uuKgXxe2Ibdu2YeLEifDx8ZGIusjMzMSIESPAMAw6duyIyZMnw9rauoHzTdqIjY2FvLw8goKCcPnyZTx8+BAuLi5YuXIlbt++DYZhWMcHl/i93/vYsWNQVVWVcD41loeSixw+fBju7u54/PgxZs2aBYZh8PLlSxQXF8PV1RUuLi4SRU1KSkpgbm4uNXlORURFRcHExAT5+fmoqalBVFQUDAwMMGfOHDx58gSnT5+Gs7MzTE1NpUIv/lE+NgYu6kYeHh7pgXc8/QXEDYHRo0dj0KBBCA8Ph7KyMnbu3NmEkv01PjbB7N69G506dYKnpydu3rz5m/25umgUJyMjA3p6eigtLcXt27excOFCGBkZQVlZGZaWlo0aeNIwrsYoLS1FTEwMOnfujAkTJrDt0pAknUd6EF8siR+baOxdioqKgpqaGkJCQqQqoWxsbCz69esHExMTaGhoSJQBP3r0KIYPH47u3btj27Zt2LNnDxwdHWFmZiY1ukN0r+7fvw8bGxs2l1NJSQmWLl0KNTU1aGtrs3nipCkKNCIiAsbGxvj555+xZ88eqKmpsVFd0sr8+fOho6ODmTNnYsaMGWjbti2CgoLYz8+fPw8PDw+0b98e27ZtY9ulJSLoY6SkpEBbWxtt2rSBnp4ezMzMUFFRgSdPnqBDhw4SxQqamvpHyGpraz+ajuHYsWNQU1PD1KlTpWpOzszMhK6uLrp06QINDQ0JG/HWrVuwsrKCvb09AgMDkZSUBAcHB5iamkqF3hDnypUrMDU1RUJCAoC69BLR0dHQ19eHhoYGevToARcXF6nSi38FaXo2eXh4uAvvePqLiOdLsLW1hZycHBYtWtSEEv01xBeIL168wKNHj9jdUaCuVLi5uTmmTZuGW7duNYWI/xj5+fmwsrJCp06doKWlBR8fH8TGxuLp06dQUlKSup2436O0tBSxsbHo2rUrhg4d2tTi8HymVFZWYtKkScjOzpYwuvfs2dOgPPiGDRvAMIxENSquIm5oe3l5gWEYDB48WCJXFVCX+27WrFlQVVVFr169OL8I+dgCorKyEh4eHhg8eLBE++3bt3Hx4kXcvHmT80d/RIjPazt27EDr1q2hpKTUIBpZ2oiLi4OxsTFbbTA5ORlKSkpo1qyZRHL+06dPw9PTE927d2er9n0OFBQU4Pz58zh79ix7jxcsWIDOnTtzrmhLdXU1LCwscOTIEfZvoC5SSLxiGAAcP34cDMNg5cqVn1zOP4v4JoOPjw9kZWXh5OTEFjMRcefOHcydOxddunSBtbU1RowYITV6sb6Tds6cOWjTpg2boF8oFKK8vByXLl3C06dPpUYv8vDw8DQ1vOPpLyKapI4ePQoVFRVYWFigX79+uHLlitTsDIgb50uWLIG1tTWaNWsGLy8vifPrW7duhYWFBaZPn86WkZVGhEIhsrOzERYWhkOHDrF5W4qKimBtbS0RFv65UFpaiujoaIwbN46zIfs80o+TkxP69u3LHjk7dOgQZGVlsWHDBgCSumbfvn2cN9Dr51zZvHkzIiIiMHDgQLi7uzdYZAF1euT9+/es/uf6GBMSEhAQEIDy8nJW5gcPHkBVVVUiUqY+0qJHRL//qVOnoKioCG1tbezfv19iY4Xr1P+tN2zYgJCQEAB175i6ujoiIyMRGRkJhmEkIp+ys7Ph6emJNm3a4MyZM59U7k/BzZs34eXlhVatWuHq1atNLU6jfPPNN1BUVGQTau/btw8tWrRokOcIAC5evMh5nSH+PAqFQqSkpCA+Ph5GRkbw8PBg0zKI9Ikoof/r16+lRi9GRkZi0aJFEtVKRVUUN2zYAKFQ2GiifmnRizw8PDxNCe94+h3EJ9D6bQcOHICCggIbgjt27Fj0798f58+f//SC/g2WLFkCLS0tpKamIicnB/b29ujevTu7aATqIp/atGmD1atXN6Gkv8/H8iM05gysqqrCL7/8guHDh8Pa2pqTu3D/BBUVFVJXPYZHOhB/nsaNGwd7e3uEhYU1euy4/rPH1QWIuJzh4eFYt24dG00RFxeH/v37w93dHXfv3mX7nTx5ks0jVP87uEh5eTkCAgLQvXt36OvrY8WKFWwUzcyZMzF58mSUlJRwfhy/x969e6GiooKkpCSsW7cOxsbG2Lt3L1shTVoIDQ1l8yk+fPgQhYWFMDU1RVhYGADg2rVraNWqFRiGwYoVK9jrzp49C29vb4njoZ8DNTU1yM3NRWBgoMQRLy4SGhoKWVlZ9tjqpk2bfrO/NOjFNWvWwM/Pj61seebMGRgaGsLDw0PCCVg/ilwaNmWDg4Ph6OgINTU1zJ8/n40Y9Pf3h4ODQxNLx8PDwyPd8I6n30A83LZ+idRXr15hzJgx2LFjh0T7wIED4e/v/0nk+yfIzMxE9+7d2WSrZ86cgZKSEvr06QNTU1M23wcAHDx4UGqcM/Hx8Xj69OlHP6+srER8fDwcHBwkSk1Ly/j+CtJg9PFIH+K7v3379oWsrCwWLlzYhBL9MwQFBaF169ZYv369xPE6kd5wcXHBiRMn4OjoCCsrK6l7v0S6bsmSJRg6dCjU1NQQERGB+fPn44svvuD8BopQKPxNx1heXh4MDAwkjtdFRERAWVmZ89Gt4uOKi4uDrq4u6xgE6ubpDh06sJVZb926BU9PT2RkZDSYw6TNyfZnaCzyhAuIjqOJ7qOXlxdkZGQwe/ZsANJtZwQFBUFXVxfr1q3DvXv32PazZ8/CyMgIo0ePRkJCAoYNGwZNTc2P5rbiMm/evMG+fftgYWGBHj16YPLkyTh+/Djk5OQa2Pw8PDw8PH8c3vHUCPXLYK9Zswb29vYYM2YMYmNj2XbxBLnihoQ07RIXFhZi3bp1qK6uxvHjx9GqVSvExMTg119/hZGRETp16tQg7wDXjab79+/DxMSEPS7YmLxCoRDp6emIjo5mdxi5utPIw8MV6us28UVFRkYGVFRUYGVlBTs7O1y6dEnqFhwiDhw4AF1dXbZiKSA59uTkZDg6OkJfXx/9+/fn7AK4/v0SLYjr35dffvkF8fHxMDU1hZOTExiG4fQGSmlpqcTf+/btw7p165Ceno43b94AqJufs7KyAEj+DklJSZyfw0RkZ2dj9uzZbEJ00TiuX7+Oli1b4rvvvsPjx4/h7OyMsWPHsvdVVN2U59PwW4VJUlNToaKiglGjRkFWVhY//fSTxOfSxPHjx6Gnp4fMzEyJdtH4z507Bzs7O1hYWGDAgAGsXuTas/gxm7B+e2FhIU6cOIFevXqha9euYBgGM2fO/FRi8vDw8Hx28I6nemzduhXt27dHZGQkAGDTpk1o2bIlli1bBnt7e1hbWyMwMJDt/zGHExedTx+T6cOHD6iursaoUaOwePFidkwjR45Et27dEBAQwDnD4fcYNWoU+vfv/4f7S6MRyMPTVCxbtozN5wTUOesVFBRYZ+/48ePRt29fiTwZ0kR0dDS7cPpYiezXr18jPz+f84llKysrkZaWxkbwiuR88OABSkpKJPo+efIE6enp8Pf35+x4FixYgLFjx7Kyz507F9ra2jA2NmarsD558gSA5D2rr+O5rPMFAgHy8vKgpKQEOTk5rFmzhv1MKBTi/fv3CAkJgbq6OgwMDGBhYcHZRf5/ibCwMCQlJbF/p6SkQEVFhY0cX7NmDRQVFXH48OGmEvFvsXXrVlhbWzdaHVf0PhUWFuLZs2ec14tlZWU4duwYm/NNNI7c3NxG++/duxfLli3j7Hh4eHh4pAEZ4pHA0dGRnJycaO/evbR69Wp68uQJ7dq1i5YuXUoHDhwgZ2dnOn36NAUFBRERkaysLAkEAiIikpH5388p/m8uIBQKWZkuXLhAR44coWvXrtGbN29IWVmZiIiePHlCAoGAZGVlqbq6mpSUlOibb76h77//nhiGIQBNOYRGEQqFEn+L7sXKlSvp+fPnlJSU9Ie+R1ZW9h+XjYfnc0H83d+1axetW7eOKisriYjo7du3tHv3btq0aRNNnDiR7dOiRQtKSEhoEnn/Li9evKAHDx6QvLw8ycnJkUAgIIZhSCAQ0OnTp+nVq1ekqalJnTt3JhkZGRIKhSQnJ9fUYjfKjh07KDo6mn744QeqrKwkOTk52rNnD1laWlJBQQHbDwC1a9eOnJ2dadOmTSQnJ0c1NTVNKHlDAJC8vDwVFBTQwoUL6cKFC/TgwQPKyMigmzdv0rx58+jFixcUEBBAz58/J4Zh2Dmivo7nms4Xf8dkZGSoR48eFBcXRy1btqRTp07R7du3iYiIYRhSUVGh2bNn06VLlyg2NpYuXLhA8vLyVFtbSwzDNNUQ/nOI2x+xsbH0/fffk7GxMQkEAhIIBJSUlERr1qyh6dOnExHR/PnzKSgoiNasWUNExEmb6rcQCoX06tUrevXqlUR7bW0tJScnU3FxMbVu3Zr09fU5rxf3799Po0ePpoMHD1JFRQUxDENpaWlkYWFBmZmZbD+RTTlmzBhaunQpycnJUW1tbVOJzcPDwyPVMJC2me9fROScefHiBa1YsYJu3LhBv/zyC+3fv5/MzMyIiKikpIQ2bNhAP/74Iw0YMIDCwsKaWOrfBwBrjC5cuJD27NlDLVq0IIFAQB07dqTg4GCysLAgX19fev78OZmamtKNGzeouLiYLl++zBoQXHOmiY/rwIEDNGjQIFJQUCAFBQUqKioiHx8f0tHRoc2bN0v05eHh+WucOHGCjhw5Qj179qTJkyez7UVFRaSpqUlExDqviYiTekOcj8mXnZ1NEydOpGnTptHs2bNJUVGRiOr0/+jRo8nHx4fGjRv3qcX9S5SXl9Pq1aspPz+fxo8fTzU1NeTn50fLly+nmTNnNrV4fxiRDhcKhRQZGUmHDx8mdXV1kpGRoeTkZFJQUCAiosTERNq5cyepq6vThg0bSF9fn/P6X1y+5ORkevnyJc2aNYuI6py48+fPp9GjR9PMmTPJ2Ni4wTVEku8dz6clJyeH9uzZQ127diUfHx9Wr9TW1jbqeOH68/gxvXjmzBkaM2YMLV68mCZOnEgtW7YkIqLKykoaMmQIjRw5kubMmfOpxf3LREZG0ubNm2nr1q1UVFREvr6+FBYWRr6+vk0tGg8PD8/nyacPsuIm9Y+hPX/+HDNmzICKikqDRLnFxcX47rvvoK+vL1H5jets3LgRrVu3Zs/nz58/HyoqKmyp36tXr2LKlCno168f3N3d2dB9rh8bvH//PjQ1NdGzZ0/4+PggPz8fwP8SpdfPR8DDw/PnycrKgqmpKTQ0NJCcnAzg48couH7sGJCUa9++fVi9ejUiIyORnZ0NAJgxYwbs7Owwb948PHv2DNnZ2XBxcYGlpaXUHLcQjbG0tBRhYWHo3bs3lJSUsHHjRonPpQWRvAKBAKtXr4aJiQkMDAwaJNBOTEyEg4MD7Ozs8PLly6YQ9Q8jfg9u3rwJMzMzWFpastVygboE43p6epg1axbu37/fFGLyNIJQKERubi4UFRWhoKDApmgAfv/d4uqRSHG54+PjsWLFCkyfPh15eXkAgOXLl0NNTQ1LlizBiRMncO7cOQwZMgTm5uZSpxcBYMeOHWjdujWUlJQkChHw8PDw8Pzz8I4nSE5CiYmJbMWbwsJCzJgxA5aWloiIiJC4pqioCDExMZzOE1GfiRMnYunSpQDqkueqqqqyuQfKyspQXV2N2tpaicSkXDQkMjMz2TK+3377LVavXo3y8nJERETA1dUVLVq0wNdff424uDh4e3tj/vz5EAgEUrfI4uHhEjU1NQgNDYW+vj4GDRrE5tiR9vdKVKVp3LhxGDRoENq3b4/Y2FiUlpZi4cKFMDU1hYyMDLp27SqRSFxadL9IhyclJUFRUREWFhaIi4tjcz5Jw/1rTEaBQIDIyEh06NABU6dORVFRkcTnW7ZswVdffSUV4wOAefPmwd3dHba2ttDQ0ECnTp0kKmjFxcWhbdu2mDRpEp4/f96EkvLUJzExERoaGhg2bBi78SXtiKp6Tp8+HYMGDYKenh5rB69cuRI2NjaQk5ODmZkZHBwcpFYvnjp1CoqKitDW1sb+/fvZnE88PDw8PP88vONJjKCgIOjo6CAqKoo1Yp8/fw5/f39YW1s3cD6J4OJEW383TSAQYPTo0Thy5AjOnDkDZWVl1ulUU1ODLVu2IDU1tdGkkVyiqKgIKioqGD58OKZPnw5VVVVcu3ZNok9iYiL8/f2hra0NhmHQrl071lHFxTHx8HCNxqrXAXW6Ljw8HGZmZvD398fbt28b7S8tpKSkQF9fny1Vv3PnTigpKeGHH34AUFeuvbq6GqdOnZKKROIfY8+ePVBXV8eGDRuwdOlSjBkzBjt37pSKRZb4s3XhwgVcu3aNjb4QCoUIDw+HjY0NfH19UVxc/LvfwUViY2Ohrq6OK1euoLi4GIWFhRgyZAh69+6NmJgYtt+mTZvg5ubG+fF8roj/7vXtvh07dkBHRweBgYF4+PDhpxbtHyUtLQ36+vrse3by5EkwDIM9e/awfYqLi3Hjxg3cv39favXi3r17oaKigqSkJKxbtw7GxsbYu3dvgwhKHh4eHp5/Bt7x9P9s2bIF2trayM3NZXeCRU4K0bE7W1tbLF++vCnF/EOIG0f37t1j/x0QEABVVVU0b96cXVgBdc6cAQMGYO3atZ9Uzj/DTz/9xO6oFRQUQElJCcrKyjh79iwAybLuAFBVVYWHDx8iKCgIhoaGCA4ObhK5eXikDXH9sX37dvj5+cHHx4etWCcQCLBmzRrY2NhgxowZrPNJmpy6IllDQ0MxcuRIAA0rUL1//77RCkdcW/QLhcLflOnGjRus0wkAKioqsGTJEjg4OODHH3/8VGL+bQIDA6GtrQ0dHR1oaGggMDAQFRUVEAqFCAsLg62tLfz9/RtEPknDc7l48WL06dNHIjK3oKAA1tbWMDY2RmxsLNtX5PDg2nP4uSP+e2/atAkTJ06El5cXVq1axbZv3rwZurq6CAwMxKNHj5pCzL9E/Xdk27ZtcHNzAwDs2rULqqqq2LRpEwDg3bt3uHv37kc3J7jC7+nFvLw8GBgYSByvi4iIgLKyMnvqgYeHh4fnn+U/63iqP9H6+PjA398fQOOG3S+//IJx48Zh2rRpnDZkxWUOCQnB4MGDkZGRAQB48+YNhg0bhtatW6OkpATv3r1DYWEhnJycYGNjw9ndqlWrVsHa2po1JG7evAmGYaCmpoZRo0ZJLDTEc4AAdYusb775BkOGDOHs+Hh4uEhQUBC0tbXh4eEBNzc3MAwDb29vlJeXo7a2FqGhobCzs8P48eNRWlra1OL+LuJ6+8OHDwCA8PBwBAYG4vjx41BWVsbmzZvZvsnJyVixYgUbLSkNpKamIikpCYcOHWLb7t69y0Z0ifRiWVkZtm3bxrnFojji9ysrKwvt2rXD2bNncf78eezevRvNmjWDp6cngLpxhYWFwdjYGGFhYU0l8p9GNMaQkBBYWlqykRaiTZaTJ0+iefPmGDhwIJKSkhpcx/PpCQ4OhqamJmbNmoXhw4ejQ4cOsLa2Zj/funUr2rZtCx8fH7x48aIJJf3rLFu2DG5ubsjJyYGKigqbEw4AYmJisHDhQlaHcpH689G+ffuwbt06pKen482bNwCAFy9eICsrC4Ck3ZyUlMTJUww8PDw8nwP/SceTuNF2/vx5AECvXr0wZcqUBn2qqqrYo1yvXr1iJyiuG37z58+HpqYmjhw5gmfPngGom1yzs7NhYWEBdXV1mJiYoFevXrCysuL8+XyR0+jmzZsA6uS8d+8etLS04OrqyhoTjZGXlwctLa3PJvcCD8+/TVZWFnR0dHDu3Dm27aeffkLz5s0REBAAoO6dXLRoEXx9fTntwKjP999/j61btwIADh06BIZhwDAMG9EF1C1cBg8ezI6Vi0ycOBG+vr7s37Nnz4aGhgaMjIygp6cHb29v9rPfOiLE9XsXExMDb29vBAYGSrSfO3cOsrKyrKNJIBBg165dnJ3Dfovr169DVlYWy5Ytk2g/evQo3N3d4eDggEGDBrHR2DxNQ25uLtq1a8dGxAiFQpw7dw4mJiawt7dn+0VFRcHNzY3zdqI4K1asYCPD79+/Dx0dnQZ6saKiAi4uLvD19eXs2BYsWICxY8eyOQjnzp0LbW1tGBsbo1OnTvD09MSTJ08ASNrx9fWGNOoRHh4eHq7zn3M8iU80ixcvhomJCV6/fo2VK1eia9eu7A6IiDt37sDDwwPXr19n27huqJ85cwZGRka4ePEiAKCyshKFhYXIyMhAWVkZamtrERMTgx07diAtLY2dYLkYESSegyQ9PR0Mw2D37t0oKysDUGcIamtrY+TIkXj58iUEAgG8vLwkqg2uXr0aenp6nK9uxMPTVNQ3sjMyMmBkZITi4mIIhUL287S0NCgqKjZ6xJXrelHE1KlT0bZtW1a3hIWFQVZWFjExMcjLy0Nubi6GDBmCnj17sjqRa4ussrIyrFmzBpqamggODsaHDx/Qt29f3LhxA48ePUJSUhLU1NQwbtw49hppXEg9f/4crq6uUFFRweTJkwHU3QuRA2bRokWwsbFpkNtJGscaGxsLeXl5BAUF4fLly3j48CFcXFywcuVK3L59GwzD4MSJE00t5n+K+huN6enp0NDQwC+//ML2qampQUZGBjp37sxWCBa/Rlr0YlRUFExMTJCfn4+amhpERUXBwMAAc+bMwZMnT3D69Gk4OzvD1NSUs3pRKBTi22+/ha2tLfz8/JCTkwNXV1dcuXIFlZWV2L59O+zt7eHq6iqxIcvDw8PD82n4zzmeRFy6dAnDhg1jd/TPnj0LW1tbeHh44NSpUwD+Z/T26dNHqgzZjIwM6OnpobS0FLdv38bChQthZGQEZWVlWFpaNjrRcnF84nLevXsXADB58mSoqakhOTmZPZZw9epVaGtrw8TEBGZmZujUqRMbwQXUhcZfvXr1k8rOwyMtiHI0AcCVK1cgEAiQk5MDGRkZCQcTADx79gzt2rXDwYMHJb6DawuQxhCN4f79+7CxsWFzOZWUlGDp0qVQU1ODtrY2zM3NMXDgQM5HgZaUlGDjxo1o1aoV3Nzc4OXlxTrTqqqqkJqaCjU1NfY4GiCdi6zMzEyMHj0azZo1w9GjRyU+W716NaytrSX0vTSTkpICbW1ttGnTBnp6ejAzM0NFRQWePHmCDh06NCikwfPvIX6EX+Twe/LkCQwNDSWigIC6CshaWlqIi4uTaJcGvSjiypUrMDU1RUJCAoA6+zc6Ohr6+vrQ0NBAjx494OLiwlm9KO7oW7t2Lfr37w83NzeMHDlSIlIwISGB/UzkfJKm+8TDw8MjzfwnHU/x8fEYOnQoHBwcJM6pHzhwAEOHDoWmpiaMjIzQpUsXmJubsxOttBjt+fn5sLKyQqdOnaClpQUfHx/Exsbi6dOnUFJSkqhMwlWOHj2KSZMmAQBmzpwJe3t79j54e3ujRYsWEs6nX3/9FUFBQVixYgW7G8cfS+Dh+W1OnjwJNzc3vHnzBgEBAWjfvj2KiopQWlqKsWPHwt7eHhcuXGD7v3nzBp07d8aBAweaUOo/xscWE5WVlfDw8MDgwYMl2m/fvo2LFy/i5s2bnK7SJD4PvXv3jl0cmpmZSfSrqqpCWloaNDQ04Ozs/KnF/NOIj6v+XJudnY3Ro0fD2NgY6enpqK6uRklJCRwcHKTuSNPvUVBQgPPnz+Ps2bPs77BgwQJ07twZhYWFTSzdf4PDhw/D3d0djx8/xqxZs8AwDF6+fIni4mK4urrCxcVFIgF1SUkJzM3NpcK2ql+ERZw5c+agTZs2bAShUChEeXk5Ll26hKdPn3JaLwKSOT5Xr14NExMTGBgYNKhSl5iYCAcHB9jZ2fGR8Dw8PDyfkP+k4yk6OhoGBgbQ0NDAlStXJD57+PAhTp06hcjISKSkpHD6GNrHEAqFyM7ORlhYGA4dOsRGNBQVFcHa2przFTuqq6vx/fffo2vXrjA3N4e6ujob8STCx8cHzZs3R3JyMptIUtygkqb7xcPTVMTGxqJfv34wMTGBhoaGRBnwo0ePYvjw4ejevTu2bduGPXv2wNHREWZmZpzb7f4tEhISEBAQgPLyclZHPHjwAKqqqti2bdtHr+PiRoO4THfu3EFZWRnKysqwceNGKCoqYu7cuRL9q6qqsHv3bgwZMoST4xEhLtu2bdswceJE+Pj4SCQ1zszMxIgRI8AwDDp27IjJkyfD2tqajfL6nJxPIm7evAkvLy+0atWKj9r9hGRmZkJXVxddunSBhoYGm1sSAG7dugUrKyvY29sjMDAQSUlJcHBwgKmpqVTpxcjISCxatEgij5+okuKGDRsgFAobjSTkoh5pTCaBQIDIyEh06NABU6dObVDtcsuWLfjqq684OR4eHh6ez5XP3vH0MWN09+7dbKJBcaOisf5cNCbqy/lbSc+rqqrwyy+/YPjw4bC2tubkeOojFArh7OwMhmEwatQotl0855OPjw9UVVURExPDRzfx8PwJxPWEl5cXGIbB4MGDUVBQINHv7NmzmDVrFlRVVdGrVy9OH7VojPLycgQEBKB79+7Q19fHihUr2ApvM2fOxOTJk1FSUiIViw9xGRcvXoxBgwYhNTUVQqEQJSUliI6ORsuWLREUFCRxnbgTnuvjnD9/PnR0dDBz5kzMmDEDbdu2lRjP+fPn4eHhgfbt20s4DT9H/V9TU4Pc3FwEBgZK2Cg8/x5CoZDVjT4+PpCVlYWTkxPu3Lkj0e/OnTuYO3cuunTpAmtra4wYMUKq9CJQl4LA0dERampqmD9/Pk6fPg0A8Pf3h4ODQxNL98cR12kXLlzAtWvXkJeXB6DufoaHh8PGxga+vr4NcsE19h08PDw8PP8en7XjSXwyefHiBR49eiThuNi+fTvMzc0xbdo03Lp1qylE/NvEx8fj6dOnH/28srIS8fHxcHBwkMiFwUXjSGTw1dbW4sOHDwgNDcXChQthbm6OqVOnsv1EicUBwMPDAwMGDPjksvLwSCvierG0tBSbN29GREQEBg4cCHd39waLLKAuWvL9+/fsOypNEYUiXbdkyRIMHToUampqiIiIwPz58/HFF1+wlU2lhSVLlkBTUxOHDx+W2MUXOZ9atWrFVqeSJuLi4mBsbMw6BpOTk6GkpIRmzZpJVO87ffo0PD090b17d3ax/DnzueSv4jrielEoFCIlJQXx8fEwMjKCh4cHGx0v0oGioguvX7+WSr0I1B2d3rdvHywsLNCjRw9MnjwZx48fh5ycHHbs2NHU4v0pAgMDoa2tDR0dHWhoaCAwMBAVFRUQCoUICwuDra0t/P39G0Q+fY6Rkjw8PDxc5bN1PIkbEUuWLIG1tTWaNWsGLy8vicSQW7duhYWFBaZPn87ukkgL9+/fh4mJCTuexpxJQqEQ6enpiI6OZo0iLhpHH9txKisrQ1RUFExNTSWcTwKBADdu3PjNa3l4eCQRf1fCw8Oxbt06Nm9MXFwc+vfvD3d3d4mjrSdPnkR5eXmj38EV6sskilyov6j45ZdfEB8fD1NTUzg5OYFhGPj7+39KUf8Wd+/eRbdu3fDjjz9KtIvG+fbtW2zcuBEMw0gcU+Mi9e/Zhg0bEBISAgA4dOgQ1NXVERkZicjISDAMIxH5lJ2dDU9PT7Rp0wZnzpz5pHLzfH6IP4tr1qyBn58f3r17B6CuSrChoSE8PDwkjjvWz+fERQfGx2zC+u2FhYU4ceIEevXqha5du4JhGMycOfNTifmXEP+9s7Ky0K5dO5w9exbnz5/H7t270axZM7awgkAgQFhYGIyNjREWFtZUIvPw8PD85/lsHU8ilixZAi0tLaSmpiInJwf29vbo3r07NmzYwPbZvn072rRpg9WrVzehpH+NUaNGoX///n+4PxcjncRZu3YtxowZA3d3dzb3wLt377B+/XqYmZlhwoQJePnyJQYPHixxBI+Li2EeHq4SFBSE1q1bY/369RLH60TRkS4uLjhx4gQcHR1hZWXFyUVVfSorK5GWlsYeuxI52B88eICSkhKJvk+ePEF6ejr8/f056YgXUV+v3b59G9ra2rh8+XKDvpWVlfjw4QPKysqQkpLC6XGJExoayuZTfPjwIQoLC2FqasouEK9du4ZWrVqBYRisWLGCve7s2bPw9vaWyEvGw/N3CAoKgq6uLtatW4d79+6x7WfPnoWRkRFGjx6NhIQEDBs2DJqamhAIBJzXjWVlZTh27FiDXGi5ubmN9t+7dy+WLVsmNfojJiYG3t7eCAwMlGg/d+4cZGVlWT0iEAiwa9cuztvAPDw8PJ8zn7XjKTMzE927d2dLgp85cwZKSkro06cPTE1N2XLaAHDw4EFOT0j1FyAiWfPz89G+fXvs3r27KcT624iPa/ny5WwVvgEDBkBGRgZJSUkA6pxP27ZtQ8eOHaGrqwsrKyv+CAIPz1/gwIED0NXVlXBeiL+HycnJcHR0hL6+Pvr37y8171l0dDQGDhyInTt3slWMkpOToa6uzkZHAo1HJnB9jL6+voiNjcWjR4+goKCA/fv3A6ibB0TjyczMRHx8vMQ8xsXFo/izFhcXB11dXfZ4HVA3T3fo0IEtdX7r1i14enoiIyOjwRxdv1oVD89f5fjx49DT00NmZqZEu+h5PXfuHOzs7GBhYYEBAwawOoPrjqeEhASoqKhgz549bORqamoqGIZhbWOg8U1JLuoPcZ4/fw5XV1eoqKhg8uTJAOruh2jzYdGiRbCxsWmQ24nLtj4PDw/P54wcfcYYGxvT1KlTycbGhk6cOEHjxo2jTZs20dChQ8nOzo7WrVtHb968oUWLFpGrqysREQkEApKVlW1iySUBQDIyMkREdODAARo0aBApKCiQrKwsaWpqUvfu3ens2bM0btw4AkAMwzSxxH8c0bhevHhBRESpqanUp08fqqiooOXLl5OXlxcJhUIaP348TZw4kYYNG0a3b98me3t7kpWVpdraWpKT+6wfYx6ef5SCggLq1KkTmZqasu+PuM748ssvaeDAgVRUVEQdO3YkGRkZqXjPpkyZQi9fvqSMjAxq2bIl1dTUkL+/P4WEhFC3bt3Yfo3pR3l5+U8p6u8irsfPnDlDqampNGrUKDI0NKQpU6bQ3LlzSVNTk/r160dERDU1NRQSEkLGxsY0ceJE9nu4eM9EOv/8+fOUl5dHS5cuJWtraxIKhSQjI0MtW7akoqIiio+PpwkTJtC8efNIRUWFHB0diWEYEggEJCMjQwzDkJKSUhOPhudz4fHjx9SmTRuysbFh20S2l0AgIFtbW0pJSaGamhrS09OTGr3o5eVFr1+/psWLF5OmpiYVFRWRr68vbdmyhfr27cv2a8zu5frY2rRpQ0FBQaSgoEB79uwhDw8PcnR0JAUFBSIiUlVVJQCkrKwscR3XbHweHh6e/wrcnlX+BCKjVZzWrVuTj48PERFt2bKF/Pz8aOLEiSQrK0umpqZ0//59evnypYSRz7UJSXxcDx48oGnTplGbNm3I0tKSAgMDqXPnzjR37lxydHQkT09P6tOnTxNL/Oc5dOgQjRgxggwMDMjJyYmIiJo1a0bfffcdERFNnjyZZGRkyMPDg3R0dEhHR4eI6pyEXDeMeHi4xosXL+jBgwess0XkbBcIBJSZmUldunQhbW1t0tTUJKI6HcT190woFFLz5s0pODiYNm3aRGvXrqWrV69SREQEzZgxo9H5gcuI5qPExES6cuUKzZ49mxwdHYmIyNvbm96/f09ubm4UEBBAQqGQsrOz6fXr15Sent6UYv8hhEIh3bhxgxwcHKi2tpZWrlxJRHUOKQBkYGBAc+bMoYiICNq5cye1atWKDh48SAzDEADOzdE8nwdCoZBevXpFr169Il1dXba9traW9uzZQ87OztS6dWuJ/tKgF2VkZGju3LmkpqZGnp6e9PbtWwoPDydfX9+mFu9PIa7Dxf/dp08fVid8/fXXtH79eho0aBCVlZXR8ePHqXXr1py/Tzw8PDz/FaTHEv8NxCehCxcu0JEjR+jatWv05s0bdqfjyZMn7AKrurqalJSU6JtvvqHvv/+eNWi5RlZWFpWWlhIR0ZIlS2j//v307Nkz8vLyolevXpGlpSXNnDmTHj9+TJ6envTjjz+SUCgkoVDYxJL/OXr16kV+fn707NkzKiwsJKK6eyovL08rVqygoKAgGj9+PP38888S1/ELEB6ej/MxPTBs2DBSUFCgNWvWUFVVFfsevX//nr777rsG75k0OGxE0QctWrQgfX19ys3Npa5du1KLFi2ourqaZGRkpE4vPnr0iOLi4mj79u304cMHtr1Xr160cuVKWrBgAR04cIAuXrxI7du3p9zcXJKTk6Pa2tomlLpxxOdXGRkZ6tGjB8XFxVHLli3p1KlTdPv2bSKqc7ipqKjQ7Nmz6dKlSxQbG0sXLlwgeXl5qq2tlapoXh5u8jE9YGJiQqWlpbRv3z4qKSkhorrnsba2lrZu3Urx8fES/aVJLxIRGRkZUUlJCamqqpKOjg5VVVU1sXR/HHEbf/v27TRlyhSaNm0abdq0iYiIevfuTbNmzaJu3bqRi4sLdevWjebMmUNlZWW0Z88eztr4PDw8PP81GEi5NhaPVlq4cCHt2bOHWrRoQQKBgDp27EjBwcFkYWFBvr6+9Pz5czI1NaUbN25QcXExXb58mV2QcM2IePPmDRkaGpK9vT3p6upSUlISZWZmkqmpKdvnhx9+oOzsbNq/fz+9fv2a2rZtS9evX2fDi7lopH/st3737h3NmDGDDhw4QCdOnCBbW1t2DDU1NbRz507y8fHhd654eP4A4u9ZSkoKPXz4kBQUFMjGxoZ69+5NX331FV27do169+5NAQEBVFBQQCtXrqSXL1/S+fPnpfY927t3L02fPp2+++47Kioqotu3b5OTkxN5enqSoqJiU4v3pzl69ChFRkbS1atX6fjx42RmZibxeUVFBTVr1oz9m4tHf8TnouTkZHr58iXNmjWLiIh27dpF8+fPp9GjR9PMmTPJ2Ni4wTVE3DwCzyN9iOvFhIQEev78OT1//pz8/f2pR48eFBISQpGRkTRr1izq27cvNW/enJYvX05FRUV04cIFzr1bf5R9+/aRt7c3bdu2jX799VfauHEjrVq1ioYPHy5Vx1WDg4MpMTGRRo8eTQKBgA4fPkxffvklhYWFERFRTk4ORUVF0cWLF2nBggU0bdo0IiKqrq5mj9/x8PDw8DQhnzqp1L/Fxo0b0bp1azYx5Pz586GiooLjx48DAK5evYopU6agX79+cHd3ZxNDcq0a2k8//cTKVlBQACUlJSgrK7NJIOtXUamqqsLDhw8RFBQEQ0NDBAcHN4ncfwTx3zo2NhbBwcH4+uuvkZqaCqAuUaynpydatGjBVrSrn7iT68kueXi4hKhK07hx4zBo0CC0b98esbGxKC0txcKFC2FqagoZGRl07dpVIpE4F5OvCoXC39TXN27cgLq6OluxtKKiAkuWLIGDgwN+/PHHTyXmX+K3xnXixAk4OTnB0tISeXl5AP6XVFz8Oi4mORaX7+bNmzAzM4OlpSUSEhLY9ri4OOjp6WHWrFm4f/9+U4jJ8x9DVNVz+vTpGDRoEPT09BAREQEAWLlyJWxsbCAnJwczMzM4ODhItV7My8uDgYEBoqOj2baIiAgoKyvj559//hQi/iPExcXB2NiYLUSQnJwMJSUlNGvWDL6+vmy/06dPw9PTE927d8fp06ebSlweHh4enkb4bBxPEydOxNKlSwHUVW1SVVVlq9aVlZWhuroatbW1ElWAuObEWLVqFaytrVlD4ubNm2AYBmpqahg1ahSKiorYviJDQ/T/iooKfPPNNxgyZAjnxlWfoKAgfPHFF5g7dy7GjBmD9u3bY+7cuQCAV69ewcvLC6qqqjh16lTTCsrDI8WkpKRAX1+fNdR37twJJSUl/PDDDwDqKrlVV1fj1KlTyM/PZ3UJ1/UHUFeVKSkpCYcOHWLb7t69y45VNJaysjJs27aNcxsM4tSv8ubv74958+axFT0BID09HcOGDYOVlRWuXbsGgJuOpo8xb948uLu7w9bWFhoaGujUqRN27NjBfh4XF4e2bdti0qRJeP78eRNKyvO5k5aWBn19fdaJe/LkSTAMgz179rB9iouLcePGDdy/f5/TerG0tFTi73379mHdunVIT0/HmzdvAAAvXrxAVlYWAEldk5SUxElHmoj6OnvDhg0ICQkBABw6dAjq6uqIjIxEZGQkGIZBUFAQ2zc7Oxuenp5o06YNzpw580nl5uHh4eH5OFLpeKpvcAsEAowePRpHjhzBmTNnoKyszDqdampqsGXLFqSmpkoYDlw12kUy3rx5E0DdDtu9e/egpaUFV1dX1phojLy8PGhpaSE/P/+TyPpXyMjIgKGhIS5cuAAA2Lt3L5SUlCR2wN++fQtnZ2cMHDiwqcTk4ZFaRLotNDQUI0eOBFDnhFJRUWH14vv375Gbm9vgWi46aCZOnCixoz179mxoaGjAyMgIenp68Pb2Zj8Tl7/+ooqLYxNn/vz50NXVxcSJEzFx4kS0adMG4eHh7OcZGRlwdXWFgYEBHjx40ISS/jliY2Ohrq6OK1euoLi4GIWFhRgyZAh69+6NmJgYtt+mTZvg5ubG+fvEI13Ut/W2bdsGNzc3AMCuXbugqqqKTZs2AQDevXuHu3fvNngGufhMLliwAGPHjkVJSQkAYO7cudDW1oaxsTE6deoET09PPHnyBIDkb1BfL3LZ+QTUzWMpKSmora3Fw4cPUVhYCFNTU4SFhQEArl27hlatWoFhGKxYsYK97uzZs/D29sbDhw+bSnQeHh4ennpwK7HRH0AoFLK5H+7fv09EdQkUdXV1ady4ceTs7Exbtmyh6dOnE1Fd7qA9e/bQw4cPJc7ncy3/kSjRo5ycHGVkZFD37t0pKSmJqqqqqEOHDnTs2DHKyckhHx8fevXqFQmFQpo4cSJFR0ez33H06FFSUFAgDQ2NphrG7/LLL7+Qvr4+WVlZUUpKCnl7e9O6devIy8uLSktLKSsri9TU1Cg5OZmOHz/e1OLy8EgFEEvVV1ZWRkRE8vLy1L59ezpx4gRNnjyZwsLCaPr06QSA0tPTKT09nd6/fy/xPVzLdVdeXk5du3al1NRUWrBgAZWWltKVK1fozJkzdOLECQoPD6eUlBQaP348ERFb+pyoYfEBro1NnJiYGEpJSaHU1FSKj4+nwYMH08uXL2nx4sW0dOlSIiJycnKiKVOm0JgxY8jAwKBpBf4TPHjwgLp160Y9e/YkNTU1at26NcXExJBQKKRVq1ZRXFwcERH5+/vT/v37pTIRPA93qW/r/fLLL0RUV4jGz8+PQkNDyd/fn4iI9u/fT3FxcVReXi5xDdd0BwCSl5engoICWrhwIV24cIEePHhAGRkZdPPmTZo3bx69ePGCAgIC6Pnz58QwDPtO1deLXMudJv7ux8fH04YNG6hNmzYkKytL7du3p3v37lFFRQV5eHgQUZ3N7OTkROnp6bRgwQL22r59+1J0dDS1b9/+k4+Bh4eHh+cjNLHj608hvusUEhKCwYMHIyMjAwDw5s0bDBs2DK1bt0ZJSQnevXuHwsJCODk5wcbGhpNh0iLEx3X37l0AwOTJk6Gmpobk5GRUVFQAqMtTpa2tDRMTE5iZmaFTp05s7gEACA4OxtWrVz+p7H+W+Ph4eHp6Ij09HcrKyti8eTP7WVpaGoKCgvD69Wu2jYs7jTw8XOX777/H1q1bAdQdR2AYBgzDID4+nu1TWlqKwYMHIyAgoKnE/FOUlJRg48aNaNWqFdzc3ODl5YXKykoAdTnuUlNToaamBk9PT/YaadIbNTU1WLJkCbuDf+jQIaipqWHt2rX45ptvICMjw+afEYfrkQqiKIuQkBBYWlqy85hozjp58iSaN2+OgQMHShwr5Go0Mo/0smLFCjb/5f3796Gjo9NAL1ZUVMDFxQW+vr6cfgZFsgkEAqxduxb9+/eHm5sbRo4ciaqqKrZfQkIC+9mzZ88krpUGsrOzMXv2bHY+E+n069evo2XLlvjuu+/w+PFjODs7Y+zYsezYxNNp8PDw8PBwC6lyPImYP38+NDU1ceTIEXZCFQgEyM7OhoWFBdTV1WFiYoJevXrBysqK04khjx49ikmTJgEAZs6cCXt7e1Zeb29vtGjRQsL59OuvvyIoKAgrVqxgnWnixgbXyc/Ph4KCAhiGQWxsLNteXl4OR0dHeHt780YDD89fZOrUqWjbti3rmAkLC4OsrCxiYmKQl5eH3NxcDBkyBD179mT1B1ffN3Hn0bt37xAdHQ19fX2YmZlJ9KuqqkJaWho0NDTg7Oz8qcX80zT2e5eUlOD+/ft49uwZTExMWEdTZmYmmjdvDoZhJJz00sT169chKyuLZcuWSbQfPXoU7u7ucHBwwKBBg6RqHuORLqKiomBiYoL8/HzU1NQgKioKBgYGmDNnDp48eYLTp0/D2dkZpqamnNeLgGSOz9WrV8PExAQGBgasnSgiMTERDg4OsLOzw8uXL5tC1D+NQCBAXl4elJSUICcnhzVr1rCfCYVCvH//HiEhIVBXV4eBgQEsLCxYm5nL94yHh4eHRwodT2fOnIGRkREuXrwIAKisrERhYSEyMjJQVlaG2tpaxMTEYMeOHUhLS2OdTVyMeKqursb333+Prl27wtzcHOrq6mzEkwgfHx80b94cycnJbCJJ8cmVi+P6Pfbt24dmzZph/vz5OHXqFE6ePInBgwdLjdHHw8M1RAuR+/fvw8bGhs3lVFJSgqVLl0JNTQ3a2towNzfHwIEDOe2MBySdTnfu3EFZWRnKysqwceNGKCoqssUIRFRVVWH37t0YMmQIp6Od6jvTKioqUFZWxralp6ejW7du7CLxypUrmDBhgsRcJo3ExsZCXl4eQUFBuHz5Mh4+fAgXFxesXLkSt2/fBsMwOHHiRFOLyfOZcuXKFZiamrK5JJ8/f846sjU0NNCjRw+4uLhIlV4Ub4uMjESHDh0wdepUiSI0ALBlyxZ89dVXnNaLjdl7ycnJ0NLSgpOTE27duiXx2fv373H//n2cOnWK0zY+Dw8PD48kUud4ysjIgJ6eHkpLS3H79m0sXLgQRkZGUFZWhqWlZaOTK1eNCKBuwnV2dgbDMBg1ahTbLopYAOqcT6qqqoiJifksdoVra2uxe/du6OnpQU9PDxYWFhg+fDjnjT4eHq7wMcdsZWUlPDw8MHjwYIn227dv4+LFi7h58yanqzQBkourxYsXY9CgQUhNTYVQKERJSQmio6PRsmVLiSpGgOR4uLjIEpdp9erVGDZsGExNTeHj44Pz588DqEuI27x5c2zfvh2//vorhg4diokTJ3K2EuufISUlBdra2mjTpg309PRgZmaGiooKPHnyBB06dGCr9fHw/FXE9WJ9W2nOnDlo06YNiouL2b7l5eW4dOkSnj59KlV68cKFC7h27RpbmU8oFCI8PBw2Njbw9fVlx/hb38EVxO9ZUlISvv/+e/bvH374Abq6uggICMD9+/cbvQbgbUYeHh4eaUHqHE/5+fmwsrJCp06doKWlBR8fH8TGxuLp06dQUlKSKInLVcTPon/48AGhoaFYuHAhzM3NMXXqVLaf+E64h4cHBgwY8Mll/Td59eoV7t27h6dPn34WCysenk9NQkICAgICUF5ezr5DDx48gKqqKrZt2/bR67i4AKnPkiVLoKmpicOHD0vs4oucT61atWLztkgTCxcuRKtWrbBr1y4kJibCysoK+vr6KCoqwuvXrzF37lwoKSnB0NAQPXv2/KyOkRQUFOD8+fM4e/Ys+wwuWLAAnTt3RmFhYRNLx/O5EBkZiUWLFuHcuXNsW0FBAaytrbFhwwYIhUKJ/JgipEEvBgYGQltbGzo6OtDQ0EBgYCAqKiogFAoRFhYGW1tb+Pv7N4h84qL+EP+9b968CTMzM1haWkpUOY6Li4Oenh5mzZol4Xzi4eHh4ZE+pM7xJBQKkZ2djbCwMBw6dAhv374FABQVFcHa2ho///xzE0v423zMsCkrK0NUVBRMTU0lnE8CgQA3btz4zWs/Fz738fHw/JOUl5cjICAA3bt3h76+PlasWIGcnBwAdfniJk+ejJKSEql8r+7evYtu3brhxx9/lGgXLZ7evn2LjRs3gmEYbNy4sSlE/Evcu3cPFhYWOHPmDIC6o3UqKioSTsLKykpcvnwZR44c+ayPkdy8eRNeXl5o1aoV54ti8EgXwcHBcHR0hJqaGubPn4/Tp08DAPz9/eHg4NDE0v05xB1GWVlZaNeuHc6ePYvz589j9+7daNasGVtYQSAQICwsDMbGxmyxAmlg3rx5cHd3h62tLTQ0NNCpUyfs2LGD/TwuLg5t27bFpEmT8Pz58yaUlIeHh4fn78AAYnW4OQIAiRK4QqGQZGRkGrQTEVVXV9ObN29o+vTp9OrVKzp37hznysM2Rnh4OF28eJGEQiHNnTuXbG1t6f379xQfH0+xsbHUtWtXioiIoAkTJpCKigrt37+fiP73W/Dw8PAIBAKSlZWlpUuX0uXLl+ncuXO0ZMkSevnyJcXHx9OBAwfIxsamqcX8Xerrtfz8fLK3t6f09HSysLCQ6FtVVUU1NTUkIyNDGRkZ5ObmRnJycp9a5L/E9evXydnZme7du0c///wzeXp60tq1a8nPz4/KysooOTmZhg0bRl988QV7jegef07U1tbSjRs3aNeuXTRlyhTq2rVrU4vE85lRXFxMJ0+epNWrV1NtbS2ZmZnR+PHjaejQobRlyxby9vZuahH/FLGxsXTu3DlSV1en8PBwtj07O5v69etHoaGhFBQUREKhkJKTk+nLL7+UCr0RFxdHc+bMoZ9//pkMDQ2pqqqKJk2aRB8+fKBp06bRlClTiIho8+bNdOzYMUpNTeVtYB4eHh4phZPaW+RcSkhIoGfPnrGTTH2nU1VVFSUnJ9OECRPo1atXlJmZSbKysiQQCD65zL+HUChk/x0SEkJhYWGkpqZGxcXF1LdvX0pOTiZVVVWaNGkS+fv708WLF8nMzIzevXtHycnJ7LX8hMvD899CXHcQ1TnmRf+JFhbLly+nHTt20Pr16yk+Pp6uX79Or169ooSEhKYQ+U8j0mvTp0+nuLg4UlJSordv39LTp0+JqM75ItojuXTpEqWmppKioiK5u7uTnJwc1dbWNpnsH6P+fSMiUlFRIRMTE9q0aRN5eXmxTiciojt37tCJEyfYMYuQhsXjn0VOTo7MzMwoNDSUdzrx/CUas/MAsO0aGho0evRoOnz4MIWHh9OtW7dozpw5JBAI6Nq1a59a3L9FQUEBHThwgPbu3Utv3rwhorqxVldXk62tLQUHB1NqaiqVlJSQjIwMjR8/nrO2cH0ePHhA3bp1o549e5Kamhq1bt2aYmJiSCgU0qpVqyguLo6IiPz9/Wn//v0kIyPTqG7l4eHh4eE+nPViPHjwgFavXk2nT58mosaNDAUFBdLS0qJRo0ZRVlYWycvLU21tLScNddHC6sWLF0RElJqaStu3b6cjR45QUFAQeXl50e7du0lVVZUmTpxIp0+fpoSEBMrOzmbHxcPD899DRkaGqqqq6MCBA1RdXU0Mw5BAICCGYejhw4f09u1bIiLS0dGhiRMn0qFDhyggIID8/Pxo/fr1TSv87yAecHvmzBlKTU0lHR0dMjQ0pClTptDcuXPp7NmzJCsrSwzDUE1NDYWEhFBOTo6EnudaxJN4BNemTZsoMTGRiIgMDQ1JVVWVgoODafbs2azTqaKigpYsWUKlpaVkaWnZZHJ/auTl5ZtaBB4pRVZWlsrLy+n48eNUVVUl0X716lX279atW9OgQYPo4sWLtHTpUlq6dClFRkY2hch/mTZt2lBQUBA5OjrSnj176NixY8QwDCkoKBARkaqqKgEgZWVlieu4aAuLEOl+RUVFqqyspOrqapKRkaGamhrS09Oj0NBQ+uWXX+iHH35gN19lZWUJAL8By8PDwyOlcPKonQh3d3d68+YN63z6Pbh+JOHQoUM0YsQIMjAwoOTkZLKysiIiopqaGvr2228pMjKSEhISyMPDQ+I6ro+Lh4fn32Xjxo2UlpZG48ePp/Hjx5OSkhLt2bOH/Pz8KDMzk7p160ZEDY8pE9XpF64v8BMTE+nKlSukpaVFixcvJqK6yKZ169ZRRkYGBQQEkFAopOzsbHr9+jXl5uZyztnUGPPnz6fdu3fTjBkzaOrUqdS6dWsSCoXUv39/KigooHHjxpGSkhKdPn2aXr16RVevXiV5eXn+SDUPzx8gMTGRvvrqK9qxYwcNHz6cmjVrRmlpaeTu7k5nzpyhvn37ElHjNlRtbS0ndYj4u19fD5w/f54iIyMpLy+P1q9fT4MGDaKysjJyd3cnFRUVSktLa6D/uc6NGzfIzMyMvv32W1q6dCnbfuzYMdq+fTsbxXXkyBHW0cbDw8PDI51wYtatP7mKjISVK1eSi4sLJSUl0bhx4373e7junOnVqxf5+fnRtm3bqLCwkIjqxi4vL08rVqwgWVlZGj9+PGlpadHAgQPZ67g+Lh4enn+XKVOm0MuXLykjI4NatmxJNTU15O/vTyEhIazTiajhcWQi7keVPHr0iOLi4ignJ4dmzpzJtvfq1YtWrlxJPXr0oN27d1Pr1q2pffv2dOzYMfZ4HRcXjiKio6MpNjaWfvrpJ+rRowcR/c8JePLkSZo/fz5dunSJ5OTkqEePHrR27VqpGBcPD1fw8vKi169f0+LFi0lTU5OKiorI19eXtmzZwjqdiBq3obj4jonbwtu3b6esrCxSUFAgMzMzmjFjBvXu3ZtmzZpFERER5OLiQh06dCBbW1sqKyuj9PR0Yhim0c0HLtO9e3fasWMH+fr6UllZGX355ZfUsmVL2rBhA9na2tLIkSOpa9eudPbsWRo0aFBTi8vDw8PD8zdo8ogn8UnywIEDNGjQIFJQUCAFBQUqKioiHx8f0tHRoc2bN0vVhPqxHet3797RjBkz6MCBA3TixAmytbVlx1VTU0M7d+4kHx8fThpFPDw8nx6RLikrK6NNmzZRWloaXb16lSIiImjGjBmfRXTM0aNHKTIykq5evUrHjx8nMzMzic8rKiqoWbNm7N9cd87U1tbS119/TV988QUtX76c7t27RxcvXqT169dTmzZtKCAggOzt7am6uprk5OTY+8f1cfHwcAVxvbdz50765ptv6O3btxQeHk5fffVVE0v39wgODqbExEQaPXo0CQQCOnz4MH355ZcUFhZGREQ5OTkUFRVFFy9epAULFtC0adOIqK7YjrRGBe3fv59mzJhBCgoKBIC0tbUpOzubXr58SYMHD6aUlBQyNTVtajF5eHh4eP4GTWrhihsODx48oGnTplGbNm3I0tKSAgMDqXPnzjR37lxydHQkT09P6tOnT1OK+4cRH1dcXBzduXOHysrKyMHBgUaOHEk7d+4khmFoyJAhdPz4cdb5JC8vz+b84BcgPDw8RHU5nmpra6lFixakr69Pubm51K1bN2rRogW70JAW59PH5HRyciI5OTmKiIggX19f2rFjB/Xo0YMEAgHJyMiQoqIi2xcA53WjnJwclZeX086dO8nIyIi2bdtGKioqZGdnR1lZWRQSEkJ2dnYSi0RpGBcPD1cQ6UU5OTkyMjKikpISUlNTIx0dHaqqqpLQGdJEfHw8paamUlpaGllbW9OePXsoJiaGoqOj6d27d7R161aysbGhqqoqkpWVpQ0bNlDHjh2pf//+Uut0IqpLrWFjY0PPnz+nmpoasrOzIxkZGdqyZQvJysqStrZ2U4vIw8PDw/M3aTIrNysri0xNTUlVVZWWLFlCLVq0oGfPntHmzZvpzJkzZGlpSVOmTCFLS0vy9PSkH3/8kWxtbYmI+5XdRPLNnz+fEhISyNPTk16+fEnz5s2jrKwsioiIoHXr1pGMjAw5OzvTwYMHyd7eXuI7+AUIDw+PCDk5Odq7dy/5+/tTeHg4FRUV0ZEjR0ggEJCnp6dULLLEnU7x8fF04cIFatGiBVlYWJCHhwcNGjSIampqaNOmTeTr60vbt28nU1PTBslkpSXqdc2aNTRt2jQKCQmhqVOnkpOTE5mbm9PBgwcpPDycysrKSF1dne0vLePi4eEKcnJytG/fPvL29qa4uDj69ddfKTg4mAQCAQ0fPpyUlJSaWsTfpb4z/sOHDzRx4kSytramH3/8kfz8/GjVqlVERBQYGEhqamoUFhbGOpo2btxIEyZMoF27dlG/fv2aahj/CHp6eqSnp0dERLdu3aI1a9ZQeno6/fTTT9S6desmlo6Hh4eH52+DJqCoqAgqKioYPnw4pk+fDlVVVVy7dk2iT2JiIvz9/aGtrQ2GYdCuXTu8e/cOACAUCptC7D9FRkYGDA0NceHCBQDA3r17oaSkhISEBLbP27dv4ezsjIEDBzaVmDw8PBxAKBRCIBB89PMbN25AXV0dGzZsAABUVFRgyZIlcHBwwI8//vipxPxHmD9/PnR1dTFx4kRMnDgRbdq0QXh4OPt5RkYGXF1dYWBggAcPHjShpL/NokWLcPXq1d/tV1xczP5bIBDA0dERHh4eUjGP8fA0Jb+nF/Py8mBgYIDo6Gi2LSIiAsrKyvj5558/hYj/GKGhoUhJSUFtbS0ePnyIwsJCmJqaIiwsDABw7do1tGrVCgzDYMWKFex1Z8+ehbe3Nx4+fNhUov/j1NTUIDc3F4GBgbh582ZTi8PDw8PD8w/xSR1PP/30E6qrqwEABQUFUFJSgrKyMs6ePQugzigXN8arqqrw8OFDBAUFwdDQEMHBwZ9S3L/Fzp070a9fPwDAvn37oKKigs2bNwMAPnz4gMzMTADAu3fvftOw4uHh+W+RmpqKpKQkHDp0iG27e/cucnJyAIDVF2VlZdi2bZtU6Y+dO3eiffv27FgSExMhLy8PRUVFLFmyhO2XlpaGoKAg1NbWNpWov8m7d+8gJyeHvn37fnRhJH5fSktLkZycDCcnJ3Tv3p2dB3nnEw9P45SWlkr8vW/fPqxbtw7p6el48+YNAODFixfIysoCIPm+JSUlcVZ3iBCXNy4uDrq6uqxeBIAzZ86gQ4cOePbsGQDg1q1b8PT0REZGRoOxVVRUfBqhPzEiPcnDw8PD83nwyc5zhYaG0sGDB+n8+fMkFArp7du37Dn877//nrp06UKtWrUiov+FHsvJyVH79u0pJCSEFBUV6eLFi1KT+0hOTo709fUpIyODpkyZQmvXrmXzN/3000+UnZ1NnTt3Jk1NTSL6eO4THh6ez5dJkyaRkpISbd26lYiI5syZQwkJCdSyZUuqrKykgwcP0o4dO6hjx44kFAqJqO4or0AgoObNm7NJZaVBf9TW1tLTp0/Jz8+PPUby9ddf06pVq+jdu3e0YsUKUlNTo7lz59KIESNoxIgRRNR4KfSmRCgUkqqqKv3yyy9kaWlJfn5+tGXLFuratatEP/H7UVBQQJcvXyY1NTX68ccf+ep1PDy/wcKFC+nRo0e0detWUldXp8DAQPrhhx9IVVWVZGVlydLSklauXEnt2rUjHR0dIvqfXpSVlSUPDw8i4p7uEEekH86fP095eXm0dOlSsra2ZnV5y5YtqaioiOLj42nChAk0b948UlFRIUdHR2IYhs1/xzCMVBwp/CtwvSIrDw8PD8+f5FN6uWpqagCA3SGura3FvXv3oKWlBVdXV3YXqzHy8vKgpaWF/Pz8TyLr3yU/Px8KCgpgGAaxsbFse3l5ORwdHeHt7c3vdvPw/IcpKyvDmjVroKmpieDgYHz48AF9+/bFjRs38OjRIyQlJUFNTQ3jxo1jr+H6Lr44jem3kpIS3L9/H8+ePYOJiQkiIiIAAJmZmWjevDkYhmEjQ7mMaC579eoV2rRpgz59+vzmkRCBQICioiL2N5Gm+8jD8ykRCoX49ttvYWtrCz8/P+Tk5MDV1RVXrlxBZWUltm/fDnt7e7i6urLRQNIU9SlCIBAgLy8PSkpKkJOTw5o1a9jPhEIh3r9/j5CQEKirq8PAwAAWFhZ8pCQPDw8Pj1TzSbbIq6qqiKguCigjI4O6d+9OSUlJVFVVRR06dKBjx45RTk4O+fj40KtXr0goFNLEiRMpOjqa/Y6jR4+SgoICaWhofAqR/zadO3emXbt2kZKSEuXn59Pp06fp1KlT5ObmRoWFhbRlyxZiGIYANLWoPDw8TUDz5s3J19eXli9fTjt27KAJEyaQgYEBdejQgQwNDWnUqFEUGxtL6enpNGHCBCIikpWVZSOfuIxQKGSTZb9//54qKyupvLyc1NXVydjYmG7evEmysrLsuJo3b06jRo2i1NRUNoqLy4gilrS0tCg3N5eePHlCfn5+dOvWrUb7y8jIUKtWrVidz9UoDB6epgQAMQxDy5Yto5EjR1J+fj6FhoaSrKwsdevWjRQVFcnHx4emTp1K7969o5kzZ9Lz589JRkZGKmwpcRllZGSoR48eFBcXRy1btqRTp07R7du3iaiu0ICKigrNnj2bLl26RLGxsXThwgWSl5en2tpavhABDw8PD49UwuBfnq3Fj4Dcu3ePOnbsSFOmTKG0tDTaunUrubm5kZKSEuXl5ZGjoyO1atWKlJSUqLy8nG7cuMGG2i5YsIA8PDyoZ8+e/6a4/ygCgYD27t1LQUFBRETUunVr0tXVpf3795O8vDynw8B5eHj+PcT14vv37ykxMZHWrFlDmpqalJuby/arrq6m9PR08vb2Jmtra0pPT28qkf8w4mNbs2YNZWVl0bNnz8jKyoq8vb3JxsaGMjMzycnJiaKiomj48OE0depU0tTUpLi4OGIYhpPH0H7rOOOrV6/I3NycDA0NGz12x8PD88cQvWdCoZDWrl1L8fHxVFFRQfn5+RJHyn744QeKjY2lqqoqSk1NJW1t7SaU+vcROdWIiJKTk+nly5c0a9YsIiLatWsXzZ8/n0aPHk0zZ84kY2PjBtcQcfvoIA8PDw8Pz+/xrzqejh07RklJSRQXF0cBAQF048YNOn78OMnLy5OPjw8lJyfTzp07WefTy5cvKSIigtTU1Cg4OJjk5OSourqaFBQU/i0RPwmvX7+mt2/fkqKiIunr63N2YcXDw/PvI+7AuHv3Lunr6xMRUVxcHM2dO5e++uorioiIYPtXV1fT/v37KS4ujjIyMjify0nEokWLaNu2bbR+/XoSCoW0YcMGKiwspKtXrxIACg0NpU2bNpGOjg6pqanRxYsXSV5evsFiiwuI37Nt27bRnTt3qKCggIKCgsjY2JhatmzJOp/at29PW7ZsoS5dujSx1Dw80kNjjl2hUEhRUVG0efNm6tu3L4WFhbG5QImItm7dSjdu3KD169dzWi+Kj+3WrVvk5eVFsrKyFBAQQF5eXkREFB8fT4sXL6bRo0fT119/zTqfeHh4eHh4Phf+NcdTTU0Nbdq0ibZv306Kior06NEjunDhAnXs2JHtM23aNNq9ezfFxMTQsGHDqEWLFhKLjs/VOSMNiYB5eHj+ecTf/W+++YYuXLhAM2bMoBEjRtC7d+9o165d9O2335KPjw+FhYWx14nrQmnQH/fv36dx48ZRZGQk9evXjzIyMujLL7+kiIgI9ihdVVUV3bx5k16+fEmOjo4kKyvLeZ2/YMECiouLI1dXV9aJtmDBAnJ3dycdHR169eoVWVlZkZKSEmVkZJChoWFTi8zDw3nEddrFixdJSUmJAFCPHj0IAEVGRlJKSgqZmprS6tWrqWXLlr/5HVwlKCiIHj9+TIWFhXTnzh3S0tKioKAg8vb2JqI659OSJUtowIABtGLFCmrTpk0TS8zDw8PDw/MP8m8mkBIKhXB2dgbDMBg1ahTbXllZyf7bx8cHqqqqiImJQVVV1b8pDg8PDw8nWLJkCTQ1NXH48GEUFRWx7SUlJYiOjkarVq0QHBzchBL+Pa5duwZdXV2Ulpbi4MGDUFZWZpOGl5aWYseOHfj1118lruF6wu2dO3eiXbt2uHr1KgAgJycHDMNAX18fERER7HgKCwsxYsQIzo+Hh4drBAYGQltbGzo6OtDQ0EBgYCAqKiogFAoRFhYGW1tb+Pv7S+hMQDqSbcfGxkJdXR1XrlxBcXExCgsLMWTIEPTu3RsxMTFsv02bNsHNzU0qE6bz8PDw8PD8Fv/41jL+P2JJIBBQRUUF9evXj3r27EnHjh0jb29v2rlzJykqKlJ5eTk1b96ctm/fTqWlpZSYmEhTpkz5p8Xh4eHh4RT37t2j1NRUio2NJRcXF7YdAKmrq9OECROIYRj6+uuvqW3btjRjxowmlPb3aSzSQEVFhUxMTGjTpk20YsUKWrt2Lfn5+RER0Z07d+jEiRPUvXt3+uKLL9hruJy7pLq6mmpqaigoKIh69uxJaWlpNGXKFIqLi6MrV67QkiVLSEZGhtzd3UlfX5/S0tKIiM/JwsPzW0Aswv3cuXOUkpJCKSkpJC8vT48fPyZvb2/69ddf6YcffqDAwEAiqjvqamhoyObOJCLOHc1tjAcPHlC3bt3YPKUyMjIUExND7u7utGrVKmIYhiZPnkz+/v7k6+vL5rniehQXDw8PDw/PH+UfPWr3sUmyvLycduzYQTt37iRLS0vauXMn2//27dvUrVs3foLl4eH5LKmv2/Lz88ne3p7S09PJwsJCom9VVRXV1NSQjIwMZWRkkJubG6ePnomPbdOmTaSiosLmLBk1ahQdOHCAvv32W1q+fDkREVVUVNDo0aOJYRg6dOgQZ3U+GskzdefOHVJTU6Oqqipyc3OjyZMn05w5c+jZs2fUrVs3kpGRoS1btpCHhwcn81Tx8HCV2NhYOnfuHKmrq1N4eDjbnp2dTf369aPQ0FAKCgoioVBIycnJ9OWXX0qNQ1ekC7777js6dOjQ/7V35/E1nvn/x1/nnEQSJIImsabRQShK1NDQlg5jKcYSW2tJiH0IpRGiDWOsaUtVVcYaSqUtQamKPfYldiFFxy4T1QYVaU6W+/eHX85XKrqTg/fz8fB4OPf6ue/k3DnX51zX52L79u04OzuTmZmJo6MjW7ZsoVWrVvj7+9O7d2+6dOmSZz8REZHHxZ/6qT+3EfHuu+/SqVMnOnTowK5duyhcuDBBQUH07t2bQ4cO0b17d65evUrz5s0ZM2aMbd9HYZpwEZHfIve52K9fP6Kjo3F2dub69eucP38euNMrJjf/v3//fmJjY3FyciIgIAAHBweysrIKLPZfknttI0aMYOLEiVy8eJH//e9/ACxbtowGDRqwaNEiwsPDGTduHC1btuT8+fOsWLHCbp/5Vqs1T53B7OxsAKpUqULp0qU5f/482dnZNG7cGIBr167Ro0cPIiIi6NixI/Bo9MAQsQeXLl1i5cqVfPbZZ3z33XfAnaSL1Wqlfv36hIWFERsbS2pqKmazmddffx2LxWJ7X9q73GdB27ZtOXToEFOmTAGwzdhstVpp0aIFhmEwb948rFZrnv1EREQeF39K4unuxsO4ceOIjIykWLFifP/997z00kvExMTg5uZGYGAgAwYMYN++ffj5+XHjxg1iYmL+Lxg7/fZbROS3urszaXx8PLGxsZQuXZoKFSrQs2dPhg0bxrZt27BYLJhMJjIzMxk3bhx79uzJ822+Pfd4Avjwww9ZsGABX375JeHh4ZQqVcrWa2vz5s20bduW/fv3s3v3bmrWrMnhw4dxdHQkKyvLrp75W7ZsAbDNohoZGUnLli35xz/+wZgxY8jIyADghx9+ICUlhaSkJI4dO8bYsWNJS0tj2LBhj1SDWMQelCtXjtDQUJo1a8ann35KXFwcJpPJ9j50c3PDMAyKFi2aZ79HpcdTrho1ajB37lwmTJjAiBEjOHDgAP/973+ZMWMGtWvX5sMPP2TTpk1s27atoEMVERF5IP6UFk1u4+Hy5csAxMbG8uKLL5Kens6//vUvunfvTk5ODq+//jo9evSgVatWnDhxgkaNGj0SMxmJiPxWud9Yf/zxxxw4cIChQ4fSrFkzAIKDg7l58yZt2rQhJCSEnJwcdu3axbfffsvatWsLMuzfJCsri+PHjzNw4EBq1qzJqVOn2LdvHx988AHlypUjJCSEadOmYbVacXBwsP2tsLdn/pw5cxgxYgTvv/8+gYGBTJ48mUmTJtG7d28yMjKYPn06O3bsYMGCBbRq1YpGjRrRv39/ihYtipeXF8uXL7cd61FrEIs8LHcPzb37/y+++KLtfTNo0CA++OADmjRpQlpaGuvXr6dUqVJ29bz4vYKCgnB1dWXgwIEsXboUwzDw9PRk2LBhpKSkULFiRTw9PQs6TBERkQfiT6vx9MUXX9C2bVt8fHyIiYmhbt26AGRmZvL2228zdepUFi1aZBu/nkvFV0XkcfXf//6XPn36sGfPHgYPHszkyZNt686ePctnn33GJ598QqlSpfD29mbWrFm24XWPSkOrR48ebN68mYkTJzJ79mxcXV2pUqUKO3bswNXVlbi4ONuwErDP2iXHjh1j9uzZbNy4kUGDBnH+/HmaNGlC06ZNATh//jwNGzbE19eXuLg44E4vNovFgr+/v75AEfkFdyea5syZw44dOyhUqBB+fn62CRR27NjBe++9x6pVq6hUqRL169fn5MmTxMfH4+TkZJfPjt/j8uXLXLx4kczMTBo0aIDZbGbUqFGsXLmSLVu2UKpUqYIOUURE5E/3pyWekpOT+fe//83s2bNZvnw5bdq0sX3QyMrKYsyYMUyaNIkNGzbYamOIiDzu1q1bx9SpUzl06BDr16/Hz88vz/r09HRcXFxsrx+1BEZycjJ9+vQhKSmJXr160bx5c2rXrs2qVat49913Wb16Ne7u7gUd5i9KSkpi5syZbNmyhatXr7Jq1Sr8/f1tRYATExPx9/fnww8/pEePHnn21RcoIr9OWFgYH3/8MR06dCA7O5s1a9bQuXNnIiMjAdizZw/Tp09n3759jBw5kj59+gB3aiHlDr97nCQmJjJlyhTWrl3Lxo0bbbPeiYiIPG5+V+smvxnoSpcuzaRJk7hx4wavv/46GzZsoH79+hiGgYODA2PHjqV8+fI0bNjwTwlcRMSe3G9mzubNm+Pg4MB7771H3759mTt3LjVr1iQ7Oxuz2YyTk5Nt29znpT0ZPXo0HTt2vG+DqHTp0qxZs4bU1FSKFy8O3LkXs2bNoly5chQrVuwhRvv7ValShX79+gEwa9Ysdu7cib+/P46OjuTk5FC2bFl8fHy4efPmPfsq6STyyxYuXEhsbCwrVqygXr16fPrpp8yfP58PP/yQGzdu8J///IcXXniBjIwMLBYLM2bMoHLlyjRs2PCxTDplZWVhtVrx9PQkPj6eatWqFXRIIiIiD8xvbuHc3biKjo4mKSmJtLQ0/va3v9GuXTvmzZuHyWSiadOmrF+/3pZ8cnR0pH///sCj942+iMjPufu5uHDhQvbu3UuRIkV4/vnn6dKlC02aNCEzM5OPPvqIvn37MmfOHJ577jkMw8iTrLK3YSQ3b94kMjKS7du3M2vWrHwbRrnXXrx4cdLS0lizZg3R0dFcuXKF1atXYzKZ7HKITH6JwurVqzN48GCsVivTpk3Dzc2Nvn37YjabKVKkCFarlczMzAKKWOTR8tP32A8//ECPHj2oV68eq1evpn///kycOBGA4cOHU6xYMSIjI22JppkzZ9KtWzeWLFnCyy+/XFCX8cA4ODjg5+dH9erV8wxHFhEReRz95uzP3dNnL1q0iK5du5KSksKbb75pG58/bdo0zGYzLVq0YNWqVTRq1CjvSZV0EpHHSO5zMSwsjMWLF9sK44aGhnL58mWGDx9OixYtMJlMzJo1izZt2rBx40b+8pe/FHDk95eTk4ObmxtXrlyhTp069O/fn6ioqHuST3c3LC9dukRCQgLFihVj9erVdluv6u4G8fLly0lJSeHGjRsEBgZSuXJlRo0ahcViYfjw4Rw8eBAPDw+OHz9OTk4OgwcPLuDoRR4Nue+xyZMnU6lSJQYMGMD58+f53//+x1tvvUV4eDhvvPEGR48epUSJErz77rsUK1aM0aNH4+/vT1ZWFs7OzpQrV66Ar+TBUtJJRESeCMbv8NVXXxkVKlQw9u7daxiGYXz22WeGs7OzsWjRIts2169fN1q0aGE0btz495xCROSRMm/ePOOZZ54x9uzZYxiGYXz88ceGo6Oj4eTkZERERNi2W7FihREaGmpkZWUVVKi/WmZmpmEYhnH16lWjXLlyxosvvmgcP378vttnZ2cb165dM3JycgzDMOz+GocNG2Z4eXkZ9erVMypUqGB4enoaixcvNnJycoyzZ88a/fr1M0qWLGn4+fkZn3/+ue1+2Pt1iRSk7Oxs2/+jo6ONMmXK2J6LhmEY8fHxRqVKlYwLFy4YhmEYiYmJRteuXY2vvvrqnvdWenr6wwlaREREHqjf9TX0lStXKF++PHXr1mXZsmUEBwczbdo0unfvzq1btzh8+DAvvvgiMTExFC1a9M/OlYmI2JWsrCzOnz9P//79bcNIBg0axMSJE7lx4wbjx4+nWLFiDBs2jLZt29K2bVvA/otS5/ZY8vDw4ODBg9SuXfu+PZ/gTg+HkiVLAnfqVdnztS1btozFixezYcMGKlasSOHChQkODiYsLAw3Nzdat25NSEgI6enpmEwmAgICMJlMdv8zEylouT2ddu/ezeHDhxkzZgz16tXLMyz32rVrLFy4kG7duvHmm2/i6upKs2bNbO8xs9mMyWTC2dm5gK9GRERE/gy/a1a7RYsWsX79erp27UqnTp145513bPWbVq5cya5duxgxYgRPPfUUcP+iuyIijyIjn5pF169f59q1azg5OdGsWTN69+7NsGHD2LFjB82aNSM9PZ2PPvrI9qy0Vz/3vL569Sq1a9emQoUK900+2aP58+dTv359qlSpYls2c+ZMlixZwubNm3FwcLANB+zSpQsHDx4kKSkJs9nMuXPn8Pb2xmw262+ZyK+Qk5PDsWPHeOGFF8jKymLChAmMGDECuPPsvHXrFu+//z5Tp07F3d2dkiVLsnv3bhwdHe2yHpyIiIj8cb/rE3TdunX5/PPPadmyJTNmzLA1pNLT04mKiuL777+3fesN6IO6iDw2cnJybA2jmzdv8uOPP3L79m3c3d2pWLEix48fx2Kx0K1bNwAKFy5M+/btiY2NtU0Nbq/uTqzMnj2bYcOG0alTJ/bv309qaiqenp4cPHiQs2fPMmDAAE6cOFHAEf+y+Ph4+vbtS1RUFGfOnLEtT01N5eLFizg7O+Pg4EB6ejoAERERpKamkpCQAICPj4+STiK/4O7vMM1mMzVr1iQ6OprixYuzZcsW27PCZDLh6urK0KFD2b9/PwsWLGDv3r04OjqSlZWlpJOIiMhj6nd9iq5SpQpLlizB2dmZkydPsnXrVrZs2UKbNm1ITk4mKirKNpORiMjj4u7kw5QpU+jatSv16tVjyJAh7NmzB4CiRYvy3//+ly+++IKUlBTefvttzGYzbdq0wWKxkJWVVZCX8LNyr23kyJFERERw69Yt0tPTadeuHUuWLCE5OdmWfLpw4QLt27fn7NmzBRz1z2vYsCHR0dHExsYyY8YMTp06BUDPnj0xm8306tULABcXFwBbEvGnw8SVdBLJ3929lGJiYpg+fToAnTt3Ztq0aRw9epT//Oc/eRK/RYsWpWLFijRq1AiLxUJ2drbdTUIgIiIif57fNdQO7tQm+eyzzwgNDQWgVKlSlClThuXLl+Po6Kg6GCLy2AoPD2f27Nl88MEH5OTkMGPGDJKTkzl06BCGYTBp0iQ++ugjSpcuTbFixdi3b98jM4xk/vz5jBs3jpUrV1KrVi327t2Lv78/5cqVY+jQoXTt2hUvLy/+97//MWDAAJYtW2a3z/q7/w4tWrSI8PBwAgICGDRoEJUqVWL+/Pm888471KhRgwkTJnDjxg3GjRvH9evX2bp1q5JNIr/g7mR8YmIi3bt3x2KxEBISQvfu3QFYuHAho0ePpkOHDgwaNIiKFSsWZMgiIiJSAH7310sWi4XXXnuNJk2acP36dZycnChfvjwmk8kup88WEfkznD59mvXr1xMbG8vLL7/MV199xcmTJ3nvvfdsQ4wnTpzI66+/TkpKCs2aNbP1dLL356LVaiUzM5PQ0FBq1arFihUr6NmzJ9HR0Rw4cICIiAjMZjMBAQGUL1+eFStWAPZZJP2nxc179OiBYRiMHj0awzAICwujW7duuLq6MmbMGOrUqYOXlxdeXl5s3rxZw+tEfoXc90doaChnz57FxcWFpKQkJkyYgNVqJTg4mMDAQODOMNbr168zfvx4ypUrV5Bhi4iIyEP2u3s83Y8+qIvI4+zo0aO0aNGCU6dOsWnTJrp27WqbYCEtLY2YmBhatWqFl5eXbR97TMxA/kXSk5KSKFasGBkZGbRp04agoCDeeOMNLly4QPXq1TGbzURFRdGlSxe77cH106E/KSkpDBkyBLjT+yK351NoaCjly5cH7szA5e7ujq+vL2az+ZFIFIrYg+joaN544w02bdpEhQoVyMjIIDAwkB9++IE+ffrQs2dPAGbNmkVcXByxsbH6nCgiIvKE+dM/VevDhIg8LvJLpLu6ulK1alU++ugjxo8fn2dWz6SkJDZs2ECNGjXyJJ7sMelktVopVKgQgK2or8Visc38Fh8fT3Z2No0bNwbg2rVr9OjRg2eeeYaOHTsC2GXS6adDfyIjI7FYLJQoUYLu3bvbel+Eh4djMpkYOHAgvr6++Pv75zmGkk4iv86ZM2eoXr06tWrVAu58Dpw/fz4BAQFMnDgRk8lEUFAQAwYMoG/fvupNKCIi8gTSJ2sRkXzc3TD66KOPcHV1pXv37lSoUAE3NzfCwsJ4++2388zqGRERgclkok6dOgUZ+s/asmULr7zyii3pFBkZyaZNm3BwcKBOnTqEh4fj5OTEDz/8QEpKCklJSZhMJsaOHUvJkiUZNmwYYL+9uH7r0J8bN27cM/RHDWKRX5bbs9DJyYkff/wRq9WKs7MzmZmZlC1blkmTJtGqVSsWL16Ms7MzXbp0wWKxYBiG3mMiIiJPmD99qJ2IyONkxIgRfPLJJwwcOJBevXpRqlQpcnJyaNiwIZcuXeK1117D2dmZrVu3cvXqVQ4dOoSjo6NdfqM/Z84cRowYwfvvv09gYCCTJ09m0qRJ9O7dm4yMDBYvXszzzz/PggUL8Pb2pmPHjmzatImiRYvi5eXFrl27cHR0LOjL+EUa+iPy8Bw7dgw/Pz/efvttxowZY1seFxfHnDlzSE1NxWw28+WXX9oS3iIiIvJkUeJJROQ+PvzwQ/71r3+xceNGatasCUBmZiaOjo5kZmYyYsQIjh8/joODA1WqVOGdd97BwcHBbusDHTt2jNmzZ7Nx40YGDRrE+fPnadKkCU2bNgXg/PnzNGzYEF9fX+Li4oA7Q+4sFgv+/v6PTJH0t956i/j4eOLj44E7PZguX75MQEAA3333HaNHjyYoKAj4v55b9pgoFHlUREdH07dvX4YOHUrnzp0pXrw4ISEh1K9fn3bt2lGtWjXWr19PkyZNCjpUERERKQD23XoQESkgWVlZHD9+nIEDB1KzZk1OnTrFvn37+OCDDyhXrhwhISFMmzYNq9WKg4ODLWlhz4mZGjVq8M9//hO409vn6tWrtGvXDriTUHv66af58ssv8ff3Z9GiRfTo0YOGDRva9s/OzrbbawMN/REpKEFBQbi6ujJw4ECWLl2KYRh4enoybNgwUlJSqFixIp6engUdpoiIiBQQfdIWEcmHg4MDt2/fZt68eSxatIhevXqxZMkSGjRowMWLFxk3bhyZmZkUKlTIlrQwDMOuEzMAVapUoV+/frzyyit8//337Ny5E8A2PLBs2bL4+Phw8+bNe/a1x5pOd8stdt62bVsOHTrElClTAGzDA61WKy1atMAwDObNm4fVas2zn4j8fgEBARw8eJDPP/+cpUuXkpCQgLOzM1FRUVgsFiWeREREnmD23UISESlAU6ZMoU+fPowbN45evXrRvHlzateuzapVq3j33XdJS0vD3d3dtr09JjDyG0JWvXp1Bg8ejNVqZdq0abi5udlmmypSpAhWq5XMzMwCiviPq1GjBnPnzqVv376kpaXZhv7MmDEjz9Cfbdu2aeiPyJ+obNmylC1bFrgzq+SUKVNYu3YtGzdupFSpUgUcnYiIiBQUJZ5E5Ik1evRoOnbsaJsG/KdKly7NmjVrSE1NpXjx4sCdRM6sWbMoV64cxYoVe4jR/nZ3J52WL19OSkoKN27cIDAwkMqVKzNq1CgsFgvDhw/n4MGDeHh4cPz4cXJychg8eHABR//HaOiPSMHJysrCarXi6elJfHw81apVK+iQREREpACpuLiIPJFu3rxJyZIl8ff3Z9asWfk2jO5O3KSlpbFmzRqio6O5fPkyBw4cwNHR0VZXyJ4NHz6cJUuW4OPjw9WrV0lLS2Pq1Km8/vrrnD9/nsmTJ7Ns2TK8vb0JDw+nbdu2ODg42ApvP8ouX77MxYsXyczMpEGDBpjNZkaNGsXKlSvZsmWLemGIPEC5kzGIiIjIk009nkTkiZOTk4ObmxtXrlyhTp069O/fn6ioqHuST3cPUbt06RIJCQkUK1aM1atX2/XsdXdbtmwZixcvZsOGDVSsWJHChQsTHBxMWFgYbm5utG7dmpCQENLT0zGZTAQEBGAymR6LpBNo6I9IQVLSSUREREA9nkTkCZWbNPr222+pXbs2Pj4++SafcuXk5JCamkqJEiXsNjEzf/586tevT5UqVWzLZs6cyZIlS9i8eTMODg62RFmXLl04ePAgSUlJmM1mzp07h7e3N2azOd+6UI+6rKwsjh07xpIlS+jZs6eG/oiIiIiIPCSPV8tCRORXyu2x5OHhwcGDBzl37hz9+/cnMTEx3+3NZjMlS5bEZDJhGIbdJZ3i4+Pp27cvUVFRnDlzxrY8NTWVixcv4uzsjIODA+np6QBERESQmppKQkICAD4+Po9t0gnu/Lz9/PyYNGmSkk4iIiIiIg/R49e6EBG5j5ycnDyvc3v/eHh4cODAAc6ePfuzyadc9ljTqWHDhkRHRxMbG8uMGTM4deoUAD179sRsNtOrVy8AXFxcALh9+zbu7u4ULVo0z3Eex6TT3TT0R0RERETk4Xq8WxgiIv/f3T15Zs+ezbBhw+jUqRP79+8nNTUVT09PDh48yNmzZxkwYAAnTpwo4Ih/vezsbAC6devG+PHjWb58OTNnzuT06dOULVuWMWPGsHv3bjp16sTp06dJSEhg3LhxlC5dOs+wPBERERERkT+bajyJyBNl5MiRREdH849//IPk5GQOHTrEyJEjCQgIoHTp0ly9epW6devi7OzMV199RYUKFQo65J+V36x6CxcuZPTo0bRv356wsDA8PDxYtWoVY8aM4fLly3h5eeHl5cXmzZtxdHR8bIfXiYiIiIhIwbPv6ZhERP5E8+fPJyYmhnXr1lGrVi327t2Lv78/kZGRWK1WunbtipeXF3v27GHAgAF4e3sXdMg/6+6kU0xMDCkpKQwZMoTAwEAAwsPDAQgNDaVjx4507NiR3bt34+7ujq+vL2az+ZGYmU9ERERERB5dam2IyBPBarWSmZlJaGgotWrVYsWKFfTs2ZPo6GgOHDhAREQEZrOZgIAAypcvz4oVKwDscvY6yDt0MDExkcjISCwWCyVKlKB79+55kk8mk4mBAwfi6+uLv79/nmMo6SQiIiIiIg+ShtqJyGMpvyFoSUlJFCtWjIyMDNq0aUNQUBBvvPEGFy5coHr16pjNZqKioujSpUu++9uj0NBQzp49S3JyMklJSXh4eBAaGkpwcDBwZ9hdREQEr7zyCuPHj6dcuXIFHLGIiIiIiDxJ9FW3iDx2rFYrhQoVAiArKwuTyYTFYrEV0o6Pjyc7O5vGjRsDcO3aNXr06MEzzzxDx44dAfucue6noqOjmTt3Lps2baJChQpkZGQQGBjIvHnzMJvN9OzZk8DAQG7fvk1cXBxlypQp6JBFREREROQJo2qyIvLY2LJlC4At6RQZGUnLli35xz/+wZgxY8jIyADghx9+ICUlhaSkJI4dO8bYsWNJS0tj2LBhWCwW2yxx9u7MmTNUr16dWrVqUaxYMUqVKsX8+fPJyclh4sSJREdHAzBgwACWL1+O2WwmJyenYIMWEREREZEnihJPIvJYmDNnDu3bt2fhwoUATJ48mQkTJlC9enUqVKjA9OnTefXVV7lw4QKtWrWiUaNG9O/fn5YtW5KcnMzs2bNtx7LHmk53yx0h7eTkxI8//ojVasVsNpOZmUnZsmWZNGkSV65cYfHixcTExAB3rskwDM1eJyIiIiIiD5VqPInIY+HYsWPMnj2bjRs3MmjQIM6fP0+TJk1o2rQpAOfPn6dhw4b4+voSFxcH3BlyZ7FY8Pf3x2KxPHIzvB07dgw/Pz/efvttxowZY1seFxfHnDlzSE1NxWw28+WXX9p6gYmIiIiIiDxMSjyJyGMjKSmJmTNnsmXLFq5evcqqVavw9/cnMzMTR0dHEhMT8ff358MPP6RHjx559rXX2et+SXR0NH379mXo0KF07tyZ4sWLExISQv369WnXrh3VqlVj/fr1NGnSpKBDFRERERGRJ9Cj89W+iMgvqFKlCv369QNg1qxZ7Ny5E39/fxwdHcnJyaFs2bL4+Phw8+bNe/Z9FJNOAEFBQbi6ujJw4ECWLl2KYRh4enoybNgwUlJSqFixIp6engUdpoiIiIiIPKGUeBKRR1ZOTs49NYuqV6/O4MGDsVqtTJs2DTc3N/r27YvZbKZIkSJYrVYyMzMLKOIHIyAggBdeeIGLFy+SmZlJgwYNMJvNREVFYbFYlHgSEREREZECo6F2IvJIujvptHz5clJSUrhx4waBgYGUKVOGc+fOERkZyccff0zXrl3x8PDg+PHjJCYmcuLEiUeqltNvlZiYyJQpU1i7di0bN26kVq1aBR2SiIiIiIg8oR7flpeIPNZyk07Dhw9nyZIl+Pj4cPXqVd5//32mTp3K66+/zogRI8jJyWHZsmV4e3sTHh5O27ZtcXBweGRrOv2SrKwsrFYrnp6exMfHU61atYIOSUREREREnmBKPInII2vZsmUsXryYDRs2ULFiRQoXLkxwcDBhYWG4ubnRunVrQkJCSE9Px2QyERAQgMlkemyTTgAODg74+flRvXp1HB0dCzocERERERF5wmmonYg8EubPn0/9+vWpUqWKbdnMmTNZsmQJmzdvxsHBwTZ8rkuXLhw8eJCkpCTMZjPnzp3D29sbs9mcb10oEREREREReTDU+hIRuxcfH0/fvn2JiorizJkztuWpqalcvHgRZ2dnHBwcSE9PByAiIoLU1FQSEhIA8PHxUdJJRERERESkAKgFJiJ2r2HDhkRHRxMbG8uMGTM4deoUAD179sRsNtOrVy8AXFxcALh9+zbu7u4ULVo0z3GUdBIREREREXm41AoTEbuWnZ0NQLdu3Rg/fjzLly9n5syZnD59mrJlyzJmzBh2795Np06dOH36NAkJCYwbN47SpUvnGZYnIiIiIiIiD5+Ki4uI3TIMI08R8B49emAYBqNHj8YwDMLCwujWrRuurq6MGTOGOnXq4OXlhZeXF5s3b9bwOhERERERkQKm4uIiYpcMw8BkMgEQExNDSkoKQ4YMAWDhwoWEh4cTEBBAaGgo5cuXB2D37t24u7vj6+uL2WwmKyvLVnBcREREREREHj61yETE7tzdSykxMZHIyEgsFgslSpSge/fuBAYGAhAeHo7JZGLgwIH4+vri7++f5xhKOomIiIiIiBQstcpExO7kJp1CQ0M5e/YsLi4uJCUlMWHCBKxWK8HBwbbkU0REBDdu3GD8+PGUK1funmOIiIiIiIhIwVHiSUTsUnR0NHPnzmXTpk1UqFCBjIwMAgMDmTdvHmazmZ49exIYGMjt27eJi4ujTJkyBR2yiIiIiIiI/IRqPImIXXrrrbeIj48nPj4euNOD6fLlywQEBPDdd98xevRogoKCgDsz31ksFhUSFxERERERsTNqoYmIXcnNhTs5OfHjjz9itVoxm81kZmZStmxZJk2axJUrV1i8eDExMTEAWCwWDMNQ0klERERERMTOqJUmInYldya7tm3bcujQIaZMmQKAo6MjAFarlRYtWmAYBvPmzcNqtebZT0REREREROyHajyJiF2qUaMGc+fOpW/fvqSlpdG5c2eKFy/OjBkzqF+/Pu3ataNatWps27aNJk2aFHS4IiIiIiIikg/VeBIRu7Z8+XIGDhxIoUKFMAwDT09Pdu3aRUpKCn//+99ZtmwZzz33XEGHKSIiIiIiIvlQjycRsWsBAQG88MILXLx4kczMTBo0aIDZbCYqKgqLxYKnp2dBhygiIiIiIiL3oR5PIvJISUxMZMqUKaxdu5aNGzdSq1atgg5JRERERERE7kM9nkTkkZGVlYXVasXT05P4+HiqVatW0CGJiIiIiIjIz1CPJxF55GRmZtpmuRMRERERERH7pcSTiIiIiIiIiIg8EOaCDkBERERERERERB5PSjyJiIiIiIiIiMgDocSTiIiIiIiIiIg8EEo8iYiIiIiIiIjIA6HEk4iIiIiIiIiIPBBKPImIiMgTZ+vWrZhMJq5fv/5Az9O9e3cmTpz4QM/xZzp37hwmk4nDhw8XdCgFzmQysXLlyj/teEFBQbRt2/YPHcNqteLj40NCQsKfE5SIiMhDoMSTiIiI2JWgoCBMJhP9+/e/Z90///lPTCYTQUFBDz+w3+jIkSOsXbuWkJCQPMsTExPp1KkTHh4eODk5UblyZSIiIrh9+/ZDjS+/REj58uVJTk6mevXqD/z8N2/eZPTo0VSpUgVnZ2dKlSpFkyZNiI2NxTCMB37+h2369OlER0fbXjdq1IihQ4f+pmMUKlSIN998k7CwsD83OBERkQdIiScRERGxO+XLlycmJob09HTbsh9//JFPPvkEb2/vAozs15sxYwYdO3akaNGitmV79uyhXr16WK1WvvzyS06dOsWECROIjo7m73//O1artQAjBovFQqlSpXBwcHig57l+/Tr169dn0aJFjBo1ioMHD7Jt2zY6d+7MiBEjuHHjxgM9f0EoVqwY7u7uf/g4Xbt2ZceOHSQmJv7xoERERB4CJZ5ERETE7tSuXZvy5csTGxtrWxYbG4u3tzd+fn55ts3IyCAkJARPT0+cnZ158cUX2b9/f55t1q5dS+XKlXFxceGVV17h3Llz95xzx44dvPTSS7i4uFC+fHlCQkJIS0uzrf/oo4+oVKkSzs7OeHl50aFDh/vGn52dzbJly2jdurVtmWEYBAcHU7VqVWJjY6lbty5PP/00HTt2ZPXq1ezevZtp06YB+Q95u379OiaTia1bt9qWHT9+nBYtWlC0aFG8vLzo3r07165ds61ftmwZNWrUwMXFhZIlS9KkSRPS0tIYO3YsCxcuZNWqVZhMJttx8ztvfHw8devWxcnJidKlSzNy5EiysrJs6xs1akRISAgjRoygRIkSlCpVirFjx9733gCEh4dz7tw59u7dS2BgIM8++yyVK1emT58+HD582JasS01NpUePHhQvXpzChQvTokULTp8+bTtOdHQ07u7urFmzBl9fXwoXLkyHDh24ffs2CxcuxMfHh+LFixMSEkJ2drZtPx8fH/7973/z2muvUaRIEcqWLcvMmTN/NuaLFy/SqVMn3N3dKVGiBG3atLH9HiUlJVG4cGE++eQT2/afffYZLi4unDhxAsjbwywoKIj4+HimT59uu/9nz56lYsWKvPvuu3nOe/jwYUwmE2fOnAGgePHiNGjQgJiYmJ+NV0RExF4o8SQiIiJ2qVevXixYsMD2ev78+fTs2fOe7UaMGMHy5ctZuHAhBw8epGLFijRr1ozvv/8euJMwaN++Pa1bt+bw4cP07t2bkSNH5jnGN998Q/PmzQkICODo0aN8+umn7Nixg0GDBgGQkJBASEgI48aN4+uvv2bdunW8/PLL94396NGj3Lhxgzp16tiWHT58mBMnTjBs2DDM5rwfwWrWrEmTJk1YunTpr74/169f529/+xt+fn4kJCSwbt06UlJS6NSpEwDJycm89tpr9OrVi5MnT7J161bat2+PYRi8+eabdOrUiebNm5OcnExycjL169e/5xyXL1/m1Vdf5a9//StHjhxh1qxZzJs3j/Hjx+fZbuHChRQpUoS9e/cSGRnJuHHj2LBhQ75x5+TkEBMTQ9euXSlTpsw964sWLWrrcRUUFERCQgJffPEFu3fvxjAMXn31VTIzM23b3759mw8++ICYmBjWrVvH1q1badeuHWvXrmXt2rV8/PHH/Oc//2HZsmV5zvPOO+9Qs2ZNDh06xMiRIxkyZMh9Y87MzKRZs2a4urqyfft2du7cSdGiRWnevDlWq5UqVarw7rvvMnDgQC5cuMClS5fo378/U6ZM4dlnn73neNOnT8ff358+ffrY7r+3t/c9v/MACxYs4OWXX6ZixYq2ZXXr1mX79u35xioiImJ3DBERERE7EhgYaLRp08a4evWq4eTkZJw7d844d+6c4ezsbHz77bdGmzZtjMDAQMMwDOPWrVuGo6OjsWTJEtv+VqvVKFOmjBEZGWkYhmGMGjXKePbZZ/OcIywszACM1NRUwzAMIzg42Ojbt2+ebbZv326YzWYjPT3dWL58ueHm5mbcvHnzV13DihUrDIvFYuTk5NiWxcTEGIBx6NChfPcJCQkxXFxcDMMwjLNnz96zbWpqqgEYW7ZsMQzDMP79738bTZs2zXOMixcvGoDx9ddfGwcOHDAA49y5c/meL/c+3+2n5w0PDzd8fX3zXMfMmTONokWLGtnZ2YZhGEbDhg2NF198Mc9x/vrXvxphYWH5njclJcUAjKlTp+a7PtepU6cMwNi5c6dt2bVr1wwXFxfjs88+MwzDMBYsWGAAxpkzZ2zb9OvXzyhcuLDxww8/2JY1a9bM6Nevn+31008/bTRv3jzP+Tp37my0aNHC9howVqxYYRiGYXz88cf33IeMjAzDxcXFiIuLsy1r2bKl8dJLLxmNGzc2mjZtmmf7n97vhg0bGkOGDMkTw+XLlw2LxWLs3bvXMIw7v8tPPfWUER0dnWe76dOnGz4+PvnfOBERETvzYAfwi4iIiPxOHh4etGzZkujoaAzDoGXLljz11FN5tvnmm2/IzMykQYMGtmWOjo7UrVuXkydPAnDy5Enq1auXZz9/f/88r48cOcLRo0dZsmSJbZlhGOTk5HD27Fn+/ve/8/TTT/PMM8/QvHlzmjdvTrt27ShcuHC+saenp+Pk5ITJZLpnnfEzhbMLFSp033U/deTIEbZs2ZKnhlSub775hqZNm9K4cWNq1KhBs2bNaNq0KR06dKB48eK/+hwnT57E398/z3U0aNCAW7ducenSJVu9reeeey7PfqVLl+bq1av5HvPnrv+n53ZwcMjzsytZsiS+vr62ny1A4cKF+ctf/mJ77eXlhY+PT5774uXldU88P/0d8Pf35/333883liNHjnDmzBlcXV3zLP/xxx/55ptvbK/nz59P5cqVMZvNJCYm5vvz/zllypShZcuWzJ8/n7p167J69WoyMjLo2LFjnu1cXFweejF6ERGR30uJJxEREbFbvXr1sg13+6UaPH/ErVu36Nev3z0z0AF4e3tTqFAhDh48yNatW1m/fj0RERGMHTuW/fv351sw+qmnnuL27dtYrVZbMqlSpUrAnYTKT+tU5S6vXLkygG0o3t1JmruHl+XG3Lp1a6ZMmXLPsUqXLo3FYmHDhg3s2rWL9evXM2PGDEaPHs3evXupUKHCr7wzv46jo2Oe1yaTiZycnHy39fDwwN3dnaSkpAd27t8Sz69x69Ytnn/++TyJyVweHh62/x85coS0tDTMZjPJycmULl36N5+rd+/edO/enWnTprFgwQI6d+58T4Lz+++/z3NeERERe6YaTyIiImK3cmvo5NbY+am//OUvFCpUiJ07d9qWZWZmsn//flttnapVq7Jv3748++3ZsyfP69q1a3PixAkqVqx4z7/cxJGDgwNNmjQhMjKSo0ePcu7cOTZv3pxv3LVq1QKwFZYG8PPzo0qVKkybNu2eJMiRI0fYuHEjQUFBwP8lM5KTk23b3F3wOzfmxMREfHx87om5SJEiwJ2ES4MGDfjXv/7FoUOHKFSoECtWrADu9K66u+B2fqpWrWqrrZRr586duLq6Uq5cuZ/d937MZjNdunRhyZIlXLly5Z71t27dIisri6pVq5KVlcXevXtt67777ju+/vrrfOsm/VY//R3Ys2cPVatWzXfb2rVrc/r0aTw9Pe+518WKFQPuJIOCgoIYPXo0QUFBdO3aNc+sjD91v/v/6quvUqRIEWbNmsW6devo1avXPdscP3483+SliIiIPVLiSUREROyWxWLh5MmTnDhxAovFcs/6IkWKMGDAAEJDQ1m3bh0nTpygT58+3L59m+DgYAD69+/P6dOnCQ0N5euvv+aTTz4hOjo6z3HCwsLYtWsXgwYN4vDhw5w+fZpVq1bZelutWbOGDz74gMOHD3P+/HkWLVpETk4Ovr6++cbt4eFB7dq12bFjh22ZyWRi7ty5nDhxgoCAAPbt28eFCxf4/PPPad26Nc2aNaNfv37AnaFUL7zwApMnT+bkyZPEx8fz1ltv5TnHP//5T77//ntee+019u/fzzfffENcXBw9e/YkOzubvXv3MnHiRBISErhw4QKxsbF8++23tuSKj48PR48e5euvv+batWv39KgCGDhwIBcvXmTw4MEkJSWxatUqxowZk2+B9N9iwoQJlC9fnnr16rFo0SJOnDjB6dOnmT9/Pn5+fty6dYtKlSrRpk0b+vTpw44dOzhy5AjdunWjbNmytGnT5nefO9fOnTuJjIzk1KlTzJw5k88//5whQ4bku23Xrl156qmnaNOmDdu3b+fs2bNs3bqVkJAQLl26BNz5PStfvjxvvfUWU6dOJTs7mzfffPO+5/fx8WHv3r2cO3eOa9eu2ZKRFouFoKAgRo0aRaVKle4ZEgiwfft2mjZt+ofvgYiIyMOgxJOIiIjYNTc3N9zc3O67fvLkyQQEBNC9e3dq167NmTNniIuLs9Uy8vb2Zvny5axcuZKaNWsSFRXFxIkT8xzjueeeIz4+nlOnTvHSSy/h5+dHRESEbdY1d3d3YmNj+dvf/kbVqlWJiopi6dKlVKtW7b5x9e7d+56hWQ0aNGDPnj1YLBZatGjB008/TadOnWjTpg2rV6/Ok1ybP38+WVlZPP/88wwdOvSemeTKlCnDzp07yc7OpmnTptSoUYOhQ4fi7u6O2WzGzc2Nbdu28eqrr1K5cmXeeust3nvvPVq0aAFAnz598PX1pU6dOnh4eOTpNZarbNmyrF27ln379lGzZk369+9PcHDwPUmw36pEiRLs2bOHbt26MX78ePz8/HjppZdYunQp77zzjq0X0YIFC3j++edp1aoV/v7+GIbB2rVr7xlK93sMHz6chIQE/Pz8GD9+PFOnTs23Vx3cqSO1bds2vL29ad++PVWrViU4OJgff/wRNzc3Fi1aZJtBz8HBgSJFirB48WLmzJnDV199le8x33zzTSwWC88++yweHh5cuHDBti44OBir1ZrvLI67d+/mxo0bdOjQ4Q/fAxERkYfBZPzaCo8iIiIi8qulp6fj6+vLp59+mm+vFYCcnByCg4OJi4sjPj7eVgdKHiwfHx+GDh3K0KFDCzqUfG3fvp3GjRtz8eJFvLy88qzr3LkzNWvWJDw8vICiExER+W3U40lERETkAXBxcWHRokVcu3btvtuYzWbmzZtHWFgY27dvf4jRiT3KyMjg0qVLjB07lo4dO96TdLJardSoUYM33nijgCIUERH57dTjSURERESeKPba4yk6Oprg4GBq1arFF198QdmyZQs6JBERkT9MiScREREREREREXkgNNROREREREREREQeCCWeRERERERERETkgVDiSUREREREREREHgglnkRERERERERE5IFQ4klERERERERERB4IJZ5EREREREREROSBUOJJREREREREREQeCCWeRERERERERETkgVDiSUREREREREREHoj/B2AMRvW36A4iAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "metrics = ['mean', 'std']\n",
    "x = np.arange(len(faithfulness_df['mode']))  # Label locations\n",
    "width = 0.2  # Bar width\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "for i, metric in enumerate(metrics):\n",
    "   ax.bar(x + i * width, faithfulness_df[metric], width, label=metric)\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels(faithfulness_df['mode'] + ' (' + faithfulness_df['question_complexity'] + ')', rotation=45, ha='right')\n",
    "ax.set_xlabel('Modes (Question Complexity)')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Grouped Bar Chart of Faithfulness Metrics by Mode and Question Complexity')\n",
    "ax.legend(title=\"Metrics\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Surprisingly faithfulness score is less in \"entity search + vector\" and \"global search + vector + fulltext \" modes\n",
    "2. We can observe that faithfulness score increases on reasoning question for most of the modes\n",
    "3. All modes struggle on simple questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>mode</th>\n",
       "      <th>answer</th>\n",
       "      <th>faithfulness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>How does the integrated focused cost leadership strategy help competitors compete with Amazon in the book industry?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>The integrated focused cost leadership strategy helps competitors like Barnes and Noble compete with Amazon in the book industry by allowing them to offer a unique value proposition. By combining cost leadership with differentiation, Barnes and Noble can focus on specific market segments or niches where they can provide distinct products or services at competitive prices. This strategy enables them to target specific customer needs effectively while maintaining cost efficiency, thereby creating a competitive advantage against Amazon's broader market approach.</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>What is the competitive strategy of Barnes and Noble in the e-commerce industry?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>Barnes and Noble in the e-commerce industry employs an integrated focused cost leadership/differentiation strategy. This strategy combines elements of cost leadership and differentiation to set itself apart from competitors like Amazon and eBay. By focusing on cost efficiency while also offering unique and differentiated products or services, Barnes and Noble aims to carve out a competitive position in the market.</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Why should online retailers like Amazon be careful about disclosing online sales taxes in their advertisements?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>Online retailers like Amazon should be cautious about disclosing online sales taxes in their advertisements to maintain transparency and compliance with regulations. By clearly stating the inclusion of sales taxes, they build trust with customers and avoid potential legal issues related to false advertising or misleading practices. Additionally, disclosing online sales taxes helps prevent any misunderstandings or surprises for customers during the checkout process, leading to a smoother shopping experience and reducing the risk of customer dissatisfaction or complaints.</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>How threatening are substitute products to Amazon's patented tech and customer service?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>Substitute products can pose a moderate threat to Amazon's patented technology and customer service. While Amazon's patents provide a level of protection for its innovations, substitute products could still impact its market share and competitive position. Amazon's strong customer service reputation may mitigate some of this threat by fostering customer loyalty. However, continuous innovation and strategic differentiation are crucial for Amazon to stay ahead of potential substitutes in the rapidly evolving e-commerce landscape.</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>What strategies are Amazon's book competitors using for market share and how does it compare to Amazon's?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>Amazon's book competitors employ various strategies to compete for market share. eBay utilizes both cost leadership and differentiation strategies, aiming to offer competitive pricing and a unique value proposition to customers. Barnes and Noble integrates a focused cost leadership/differentiation strategy, focusing on specific market segments while offering differentiated products and services. Wal-Mart, as the world's largest retail chain, primarily focuses on a cost leadership approach, leveraging its scale to provide competitive prices.\\n\\nIn comparison, Amazon employs a mix of strategies, heavily investing in technologies like video content, cloud services, and its Kindle product. It leverages technologies such as Elastic Block Storage (EBS) and 1-Click Ordering to enhance operations and customer experience. Amazon's strategic focus on innovation, technology, and global market expansion sets it apart from its competitors, allowing it to maintain a strong position in the e-commerce landscape.</td>\n",
       "      <td>0.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>What strategies do Amazon's book competitors use to attract price-sensitive customers and grow market share, leveraging online shopping and unique, cheap books?</td>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>Amazon's book competitors employ various strategies to attract price-sensitive customers and expand their market share in the realm of online shopping. eBay, Barnes and Noble, and Wal-Mart utilize different approaches to cater to this segment:\\n\\n1. **eBay**: Utilizes both cost leadership and differentiation strategies to appeal to price-sensitive customers. It offers a wide range of products, including books, at competitive prices while also providing a unique platform for auctions and second-hand items.\\n\\n2. **Barnes and Noble**: Integrates a focused cost leadership/differentiation strategy to stand out in the market. By offering a mix of affordable books along with unique and high-quality selections, they target price-sensitive customers seeking value and variety.\\n\\n3. **Wal-Mart**: Focuses on a cost leadership approach, leveraging its vast retail chain to provide competitive pricing on books and other products. By emphasizing affordability and a wide selection, Wal-Mart aims to attract price-sensitive customers looking for budget-friendly options.\\n\\nThese competitors aim to leverage online shopping platforms to reach a broader audience of price-sensitive customers while offering unique and affordable book selections to enhance their market share in the competitive e-commerce landscape.</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                            question  \\\n",
       "10                                               How does the integrated focused cost leadership strategy help competitors compete with Amazon in the book industry?   \n",
       "16                                                                                  What is the competitive strategy of Barnes and Noble in the e-commerce industry?   \n",
       "22                                                   Why should online retailers like Amazon be careful about disclosing online sales taxes in their advertisements?   \n",
       "28                                                                           How threatening are substitute products to Amazon's patented tech and customer service?   \n",
       "57                                                         What strategies are Amazon's book competitors using for market share and how does it compare to Amazon's?   \n",
       "69  What strategies do Amazon's book competitors use to attract price-sensitive customers and grow market share, leveraging online shopping and unique, cheap books?   \n",
       "\n",
       "                             mode  \\\n",
       "10  global search+vector+fulltext   \n",
       "16  global search+vector+fulltext   \n",
       "22  global search+vector+fulltext   \n",
       "28  global search+vector+fulltext   \n",
       "57  global search+vector+fulltext   \n",
       "69  global search+vector+fulltext   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               answer  \\\n",
       "10                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The integrated focused cost leadership strategy helps competitors like Barnes and Noble compete with Amazon in the book industry by allowing them to offer a unique value proposition. By combining cost leadership with differentiation, Barnes and Noble can focus on specific market segments or niches where they can provide distinct products or services at competitive prices. This strategy enables them to target specific customer needs effectively while maintaining cost efficiency, thereby creating a competitive advantage against Amazon's broader market approach.   \n",
       "16                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Barnes and Noble in the e-commerce industry employs an integrated focused cost leadership/differentiation strategy. This strategy combines elements of cost leadership and differentiation to set itself apart from competitors like Amazon and eBay. By focusing on cost efficiency while also offering unique and differentiated products or services, Barnes and Noble aims to carve out a competitive position in the market.   \n",
       "22                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Online retailers like Amazon should be cautious about disclosing online sales taxes in their advertisements to maintain transparency and compliance with regulations. By clearly stating the inclusion of sales taxes, they build trust with customers and avoid potential legal issues related to false advertising or misleading practices. Additionally, disclosing online sales taxes helps prevent any misunderstandings or surprises for customers during the checkout process, leading to a smoother shopping experience and reducing the risk of customer dissatisfaction or complaints.   \n",
       "28                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Substitute products can pose a moderate threat to Amazon's patented technology and customer service. While Amazon's patents provide a level of protection for its innovations, substitute products could still impact its market share and competitive position. Amazon's strong customer service reputation may mitigate some of this threat by fostering customer loyalty. However, continuous innovation and strategic differentiation are crucial for Amazon to stay ahead of potential substitutes in the rapidly evolving e-commerce landscape.   \n",
       "57                                                                                                                                                                                                                                                                                                                Amazon's book competitors employ various strategies to compete for market share. eBay utilizes both cost leadership and differentiation strategies, aiming to offer competitive pricing and a unique value proposition to customers. Barnes and Noble integrates a focused cost leadership/differentiation strategy, focusing on specific market segments while offering differentiated products and services. Wal-Mart, as the world's largest retail chain, primarily focuses on a cost leadership approach, leveraging its scale to provide competitive prices.\\n\\nIn comparison, Amazon employs a mix of strategies, heavily investing in technologies like video content, cloud services, and its Kindle product. It leverages technologies such as Elastic Block Storage (EBS) and 1-Click Ordering to enhance operations and customer experience. Amazon's strategic focus on innovation, technology, and global market expansion sets it apart from its competitors, allowing it to maintain a strong position in the e-commerce landscape.   \n",
       "69  Amazon's book competitors employ various strategies to attract price-sensitive customers and expand their market share in the realm of online shopping. eBay, Barnes and Noble, and Wal-Mart utilize different approaches to cater to this segment:\\n\\n1. **eBay**: Utilizes both cost leadership and differentiation strategies to appeal to price-sensitive customers. It offers a wide range of products, including books, at competitive prices while also providing a unique platform for auctions and second-hand items.\\n\\n2. **Barnes and Noble**: Integrates a focused cost leadership/differentiation strategy to stand out in the market. By offering a mix of affordable books along with unique and high-quality selections, they target price-sensitive customers seeking value and variety.\\n\\n3. **Wal-Mart**: Focuses on a cost leadership approach, leveraging its vast retail chain to provide competitive pricing on books and other products. By emphasizing affordability and a wide selection, Wal-Mart aims to attract price-sensitive customers looking for budget-friendly options.\\n\\nThese competitors aim to leverage online shopping platforms to reach a broader audience of price-sensitive customers while offering unique and affordable book selections to enhance their market share in the competitive e-commerce landscape.   \n",
       "\n",
       "    faithfulness  \n",
       "10      0.250000  \n",
       "16      0.333333  \n",
       "22      0.000000  \n",
       "28      0.300000  \n",
       "57      0.454545  \n",
       "69      0.142857  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check question answer for global search where it has scored low\n",
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "eval_df[[\"question\",\"mode\",\"answer\",\"faithfulness\"]][(eval_df['mode']==\"global search+vector+fulltext\") & (eval_df['faithfulness'] <0.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Answer Relevancy\n",
    "\n",
    "Scores the relevancy of the answer according to the given question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.944288</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>0.970096</td>\n",
       "      <td>0.028123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.967252</td>\n",
       "      <td>0.953466</td>\n",
       "      <td>0.985486</td>\n",
       "      <td>0.011735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.976775</td>\n",
       "      <td>0.950704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.939813</td>\n",
       "      <td>0.930814</td>\n",
       "      <td>0.955302</td>\n",
       "      <td>0.009615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.954965</td>\n",
       "      <td>0.934895</td>\n",
       "      <td>0.974246</td>\n",
       "      <td>0.014387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.969242</td>\n",
       "      <td>0.924026</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.934088</td>\n",
       "      <td>0.900393</td>\n",
       "      <td>0.967728</td>\n",
       "      <td>0.029599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.953706</td>\n",
       "      <td>0.933291</td>\n",
       "      <td>0.966764</td>\n",
       "      <td>0.014332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.975329</td>\n",
       "      <td>0.973230</td>\n",
       "      <td>0.978811</td>\n",
       "      <td>0.003037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.930291</td>\n",
       "      <td>0.897861</td>\n",
       "      <td>0.957943</td>\n",
       "      <td>0.026410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.764748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.974305</td>\n",
       "      <td>0.427745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.944675</td>\n",
       "      <td>0.914451</td>\n",
       "      <td>0.989475</td>\n",
       "      <td>0.033573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.942806</td>\n",
       "      <td>0.922784</td>\n",
       "      <td>0.955302</td>\n",
       "      <td>0.013306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.935275</td>\n",
       "      <td>0.892618</td>\n",
       "      <td>0.962012</td>\n",
       "      <td>0.026796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.972342</td>\n",
       "      <td>0.926690</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.032359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.935297</td>\n",
       "      <td>0.914959</td>\n",
       "      <td>0.955338</td>\n",
       "      <td>0.013688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.956708</td>\n",
       "      <td>0.934895</td>\n",
       "      <td>0.985504</td>\n",
       "      <td>0.018364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.968694</td>\n",
       "      <td>0.924069</td>\n",
       "      <td>0.997744</td>\n",
       "      <td>0.031505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity      mean       min  \\\n",
       "0            entity search+vector       multi_context  0.944288  0.897861   \n",
       "1            entity search+vector           reasoning  0.967252  0.953466   \n",
       "2            entity search+vector              simple  0.976775  0.950704   \n",
       "3                        fulltext       multi_context  0.939813  0.930814   \n",
       "4                        fulltext           reasoning  0.954965  0.934895   \n",
       "5                        fulltext              simple  0.969242  0.924026   \n",
       "6   global search+vector+fulltext       multi_context  0.934088  0.900393   \n",
       "7   global search+vector+fulltext           reasoning  0.953706  0.933291   \n",
       "8   global search+vector+fulltext              simple  0.975329  0.973230   \n",
       "9                    graph+vector       multi_context  0.930291  0.897861   \n",
       "10                   graph+vector           reasoning  0.764748  0.000000   \n",
       "11                   graph+vector              simple  0.944675  0.914451   \n",
       "12          graph+vector+fulltext       multi_context  0.942806  0.922784   \n",
       "13          graph+vector+fulltext           reasoning  0.935275  0.892618   \n",
       "14          graph+vector+fulltext              simple  0.972342  0.926690   \n",
       "15                         vector       multi_context  0.935297  0.914959   \n",
       "16                         vector           reasoning  0.956708  0.934895   \n",
       "17                         vector              simple  0.968694  0.924069   \n",
       "\n",
       "         max       std  \n",
       "0   0.970096  0.028123  \n",
       "1   0.985486  0.011735  \n",
       "2   1.000000  0.020596  \n",
       "3   0.955302  0.009615  \n",
       "4   0.974246  0.014387  \n",
       "5   1.000000  0.032231  \n",
       "6   0.967728  0.029599  \n",
       "7   0.966764  0.014332  \n",
       "8   0.978811  0.003037  \n",
       "9   0.957943  0.026410  \n",
       "10  0.974305  0.427745  \n",
       "11  0.989475  0.033573  \n",
       "12  0.955302  0.013306  \n",
       "13  0.962012  0.026796  \n",
       "14  0.999998  0.032359  \n",
       "15  0.955338  0.013688  \n",
       "16  0.985504  0.018364  \n",
       "17  0.997744  0.031505  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer_rel_df = eval_df.groupby(['mode', 'question_complexity']).agg({\n",
    "   'answer_relevancy': ['mean', 'min', 'max', 'std']\n",
    "}).reset_index()\n",
    "answer_rel_df.columns = ['mode', 'question_complexity', 'mean', 'min', 'max', 'std']\n",
    "answer_rel_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "metrics = ['mean', 'std']\n",
    "x = np.arange(len(answer_rel_df['mode']))  # Label locations\n",
    "width = 0.2  # Bar width\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "for i, metric in enumerate(metrics):\n",
    "   ax.bar(x + i * width, answer_rel_df[metric], width, label=metric)\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels(answer_rel_df['mode'] + ' (' + answer_rel_df['question_complexity'] + ')', rotation=45, ha='right')\n",
    "ax.set_xlabel('Modes (Question Complexity)')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Grouped Bar Chart of Answer Relevancy Metrics by Mode and Question Complexity')\n",
    "ax.legend(title=\"Metrics\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.All modes score fairly for answer relevancy  except \" graph + vector \" mode for reasoning questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Answer Correctness\n",
    "\n",
    "Measures answer correctness compared to ground truth as a combination of factuality and semantic similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.742496</td>\n",
       "      <td>0.523471</td>\n",
       "      <td>0.992269</td>\n",
       "      <td>0.170004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.803536</td>\n",
       "      <td>0.565711</td>\n",
       "      <td>0.943629</td>\n",
       "      <td>0.147004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.537766</td>\n",
       "      <td>0.172675</td>\n",
       "      <td>0.733410</td>\n",
       "      <td>0.251963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.803399</td>\n",
       "      <td>0.437395</td>\n",
       "      <td>0.995587</td>\n",
       "      <td>0.208676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.853349</td>\n",
       "      <td>0.619032</td>\n",
       "      <td>0.996704</td>\n",
       "      <td>0.187401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.672318</td>\n",
       "      <td>0.175373</td>\n",
       "      <td>0.940164</td>\n",
       "      <td>0.339199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.735539</td>\n",
       "      <td>0.609525</td>\n",
       "      <td>0.909184</td>\n",
       "      <td>0.123181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.727843</td>\n",
       "      <td>0.488364</td>\n",
       "      <td>0.846143</td>\n",
       "      <td>0.162029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.394594</td>\n",
       "      <td>0.172125</td>\n",
       "      <td>0.665870</td>\n",
       "      <td>0.250464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.742180</td>\n",
       "      <td>0.491398</td>\n",
       "      <td>0.918170</td>\n",
       "      <td>0.188076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.858412</td>\n",
       "      <td>0.670062</td>\n",
       "      <td>0.996556</td>\n",
       "      <td>0.137474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.559433</td>\n",
       "      <td>0.174966</td>\n",
       "      <td>0.845223</td>\n",
       "      <td>0.300916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.736784</td>\n",
       "      <td>0.437216</td>\n",
       "      <td>0.990246</td>\n",
       "      <td>0.237939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.786999</td>\n",
       "      <td>0.455712</td>\n",
       "      <td>0.946683</td>\n",
       "      <td>0.190876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.643641</td>\n",
       "      <td>0.175082</td>\n",
       "      <td>0.934310</td>\n",
       "      <td>0.339174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.813315</td>\n",
       "      <td>0.544215</td>\n",
       "      <td>0.991202</td>\n",
       "      <td>0.156961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.677124</td>\n",
       "      <td>0.475465</td>\n",
       "      <td>0.823299</td>\n",
       "      <td>0.126854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.584535</td>\n",
       "      <td>0.175324</td>\n",
       "      <td>0.817812</td>\n",
       "      <td>0.292301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity      mean       min  \\\n",
       "0            entity search+vector       multi_context  0.742496  0.523471   \n",
       "1            entity search+vector           reasoning  0.803536  0.565711   \n",
       "2            entity search+vector              simple  0.537766  0.172675   \n",
       "3                        fulltext       multi_context  0.803399  0.437395   \n",
       "4                        fulltext           reasoning  0.853349  0.619032   \n",
       "5                        fulltext              simple  0.672318  0.175373   \n",
       "6   global search+vector+fulltext       multi_context  0.735539  0.609525   \n",
       "7   global search+vector+fulltext           reasoning  0.727843  0.488364   \n",
       "8   global search+vector+fulltext              simple  0.394594  0.172125   \n",
       "9                    graph+vector       multi_context  0.742180  0.491398   \n",
       "10                   graph+vector           reasoning  0.858412  0.670062   \n",
       "11                   graph+vector              simple  0.559433  0.174966   \n",
       "12          graph+vector+fulltext       multi_context  0.736784  0.437216   \n",
       "13          graph+vector+fulltext           reasoning  0.786999  0.455712   \n",
       "14          graph+vector+fulltext              simple  0.643641  0.175082   \n",
       "15                         vector       multi_context  0.813315  0.544215   \n",
       "16                         vector           reasoning  0.677124  0.475465   \n",
       "17                         vector              simple  0.584535  0.175324   \n",
       "\n",
       "         max       std  \n",
       "0   0.992269  0.170004  \n",
       "1   0.943629  0.147004  \n",
       "2   0.733410  0.251963  \n",
       "3   0.995587  0.208676  \n",
       "4   0.996704  0.187401  \n",
       "5   0.940164  0.339199  \n",
       "6   0.909184  0.123181  \n",
       "7   0.846143  0.162029  \n",
       "8   0.665870  0.250464  \n",
       "9   0.918170  0.188076  \n",
       "10  0.996556  0.137474  \n",
       "11  0.845223  0.300916  \n",
       "12  0.990246  0.237939  \n",
       "13  0.946683  0.190876  \n",
       "14  0.934310  0.339174  \n",
       "15  0.991202  0.156961  \n",
       "16  0.823299  0.126854  \n",
       "17  0.817812  0.292301  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer_cor_df = eval_df.groupby(['mode', 'question_complexity']).agg({\n",
    "   'answer_correctness': ['mean', 'min', 'max', 'std']\n",
    "}).reset_index()\n",
    "answer_cor_df.columns = ['mode', 'question_complexity', 'mean', 'min', 'max', 'std']\n",
    "answer_cor_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "metrics = ['mean', 'std']\n",
    "x = np.arange(len(answer_cor_df['mode']))  # Label locations\n",
    "width = 0.2  # Bar width\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "for i, metric in enumerate(metrics):\n",
    "   ax.bar(x + i * width, answer_cor_df[metric], width, label=metric)\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels(answer_cor_df['mode'] + ' (' + answer_cor_df['question_complexity'] + ')', rotation=45, ha='right')\n",
    "ax.set_xlabel('Modes (Question Complexity)')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Grouped Bar Chart of Answer Correctness Metrics by Mode and Question Complexity')\n",
    "ax.legend(title=\"Metrics\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All modes struggle on simple questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Context Precision\n",
    "\n",
    "Context Precision is a metric that evaluates whether all of the ground-truth relevant items present in the contexts are ranked higher or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity      mean  min  max  \\\n",
       "0            entity search+vector       multi_context  0.800000  0.0  1.0   \n",
       "1            entity search+vector           reasoning  0.800000  0.0  1.0   \n",
       "2            entity search+vector              simple  0.500000  0.0  1.0   \n",
       "3                        fulltext       multi_context  0.833333  0.0  1.0   \n",
       "4                        fulltext           reasoning  1.000000  1.0  1.0   \n",
       "5                        fulltext              simple  1.000000  1.0  1.0   \n",
       "6   global search+vector+fulltext       multi_context  0.800000  0.0  1.0   \n",
       "7   global search+vector+fulltext           reasoning  0.500000  0.0  1.0   \n",
       "8   global search+vector+fulltext              simple  0.666667  0.0  1.0   \n",
       "9                    graph+vector       multi_context  0.833333  0.0  1.0   \n",
       "10                   graph+vector           reasoning  1.000000  1.0  1.0   \n",
       "11                   graph+vector              simple  1.000000  1.0  1.0   \n",
       "12          graph+vector+fulltext       multi_context  0.833333  0.0  1.0   \n",
       "13          graph+vector+fulltext           reasoning  1.000000  1.0  1.0   \n",
       "14          graph+vector+fulltext              simple  1.000000  1.0  1.0   \n",
       "15                         vector       multi_context  0.833333  0.0  1.0   \n",
       "16                         vector           reasoning  1.000000  1.0  1.0   \n",
       "17                         vector              simple  0.750000  0.0  1.0   \n",
       "\n",
       "         std  \n",
       "0   0.447214  \n",
       "1   0.447214  \n",
       "2   0.577350  \n",
       "3   0.408248  \n",
       "4   0.000000  \n",
       "5   0.000000  \n",
       "6   0.447214  \n",
       "7   0.577350  \n",
       "8   0.577350  \n",
       "9   0.408248  \n",
       "10  0.000000  \n",
       "11  0.000000  \n",
       "12  0.408248  \n",
       "13  0.000000  \n",
       "14  0.000000  \n",
       "15  0.408248  \n",
       "16  0.000000  \n",
       "17  0.500000  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context_precision_df = eval_df.groupby(['mode', 'question_complexity']).agg({\n",
    "   'context_precision': ['mean', 'min', 'max', 'std']\n",
    "}).reset_index()\n",
    "context_precision_df.columns = ['mode', 'question_complexity', 'mean', 'min', 'max', 'std']\n",
    "context_precision_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "metrics = ['mean', 'std']\n",
    "x = np.arange(len(context_precision_df['mode']))  # Label locations\n",
    "width = 0.2  # Bar width\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "for i, metric in enumerate(metrics):\n",
    "   ax.bar(x + i * width, context_precision_df[metric], width, label=metric)\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels(context_precision_df['mode'] + ' (' + context_precision_df['question_complexity'] + ')', rotation=45, ha='right')\n",
    "ax.set_xlabel('Modes (Question Complexity)')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Grouped Bar Chart of Context Precision Metrics by Mode and Question Complexity')\n",
    "ax.legend(title=\"Metrics\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. All modes except entity search + vector score fairly for reasoning questions .\n",
    "2. As the metrics calculate context precision we can see for multi context question the score varies for all modes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Context Recall\n",
    "\n",
    "Context recall measures the extent to which the retrieved context aligns with the annotated answer, treated as the ground truth. It is computed using question, ground truth and the retrieved context, and the values range between 0 and 1, with higher values indicating better performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>question_complexity</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.561905</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.310712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.433013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>entity search+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.511905</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.341731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>global search+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.577350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>graph+vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.952381</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>graph+vector+fulltext</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>vector</td>\n",
       "      <td>multi_context</td>\n",
       "      <td>0.910714</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.138781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>vector</td>\n",
       "      <td>reasoning</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>vector</td>\n",
       "      <td>simple</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             mode question_complexity      mean       min  \\\n",
       "0            entity search+vector       multi_context  0.561905  0.142857   \n",
       "1            entity search+vector           reasoning  0.250000  0.000000   \n",
       "2            entity search+vector              simple  0.500000  0.000000   \n",
       "3                        fulltext       multi_context  0.952381  0.714286   \n",
       "4                        fulltext           reasoning  1.000000  1.000000   \n",
       "5                        fulltext              simple  1.000000  1.000000   \n",
       "6   global search+vector+fulltext       multi_context  0.511905  0.142857   \n",
       "7   global search+vector+fulltext           reasoning  0.125000  0.000000   \n",
       "8   global search+vector+fulltext              simple  0.666667  0.000000   \n",
       "9                    graph+vector       multi_context  0.952381  0.714286   \n",
       "10                   graph+vector           reasoning  1.000000  1.000000   \n",
       "11                   graph+vector              simple  1.000000  1.000000   \n",
       "12          graph+vector+fulltext       multi_context  0.952381  0.714286   \n",
       "13          graph+vector+fulltext           reasoning  1.000000  1.000000   \n",
       "14          graph+vector+fulltext              simple  1.000000  1.000000   \n",
       "15                         vector       multi_context  0.910714  0.714286   \n",
       "16                         vector           reasoning  1.000000  1.000000   \n",
       "17                         vector              simple  1.000000  1.000000   \n",
       "\n",
       "    max       std  \n",
       "0   1.0  0.310712  \n",
       "1   1.0  0.433013  \n",
       "2   1.0  0.577350  \n",
       "3   1.0  0.116642  \n",
       "4   1.0  0.000000  \n",
       "5   1.0  0.000000  \n",
       "6   1.0  0.341731  \n",
       "7   0.5  0.250000  \n",
       "8   1.0  0.577350  \n",
       "9   1.0  0.116642  \n",
       "10  1.0  0.000000  \n",
       "11  1.0  0.000000  \n",
       "12  1.0  0.116642  \n",
       "13  1.0  0.000000  \n",
       "14  1.0  0.000000  \n",
       "15  1.0  0.138781  \n",
       "16  1.0  0.000000  \n",
       "17  1.0  0.000000  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "context_recall_df = eval_df.groupby(['mode', 'question_complexity']).agg({\n",
    "   'context_recall': ['mean', 'min', 'max', 'std']\n",
    "}).reset_index()\n",
    "context_recall_df.columns = ['mode', 'question_complexity', 'mean', 'min', 'max', 'std']\n",
    "context_recall_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouped bar chart\n",
    "metrics = ['mean', 'std']\n",
    "x = np.arange(len(context_recall_df['mode']))  # Label locations\n",
    "width = 0.2  # Bar width\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "for i, metric in enumerate(metrics):\n",
    "   ax.bar(x + i * width, context_recall_df[metric], width, label=metric)\n",
    "ax.set_xticks(x + width * 1.5)\n",
    "ax.set_xticklabels(context_recall_df['mode'] + ' (' + context_recall_df['question_complexity'] + ')', rotation=45, ha='right')\n",
    "ax.set_xlabel('Modes (Question Complexity)')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Grouped Bar Chart of Context  Recall Metrics by Mode and Question Complexity')\n",
    "ax.legend(title=\"Metrics\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. \"entity search + vector\" and \"global search + vector + fulltext \" modes are performing poor for all question complexities. other modes have scored perfect score for context recall."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "myenv312",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
